• List of Articles Forecasting

      • Open Access Article

        1 - Studying the concepts and basics of back-casting and comparing it with forecasting and visioning
        معصومه کاظمی حسن gH نفیسه کاظمی اعظم BA
        One of the most important goals of future study is to develop knowledge and to conceive a complicated set of possible futures and, as a result, paving the ground to create and to expand the best guidelines to face with confronting challenges. As a result, active and pro More
        One of the most important goals of future study is to develop knowledge and to conceive a complicated set of possible futures and, as a result, paving the ground to create and to expand the best guidelines to face with confronting challenges. As a result, active and provocative decision making is ideal under such circumstances. Due to its normative nature and its emphasis on desired futures and considering uncertain conditions, back-casting is one of the best approaches to confront such challenges. Present study is conducted to investigate this approach on growing future study field. In this research, back-casting emergence trend, types of back-casting, steps of back-casting and its functionality are explained and this approach is contemplated. Also, forecasting and visioning methods are briefly reviewed and, ultimately, back-casting is compared with forecasting format as a contrary approach by considering a philosophical view against lack of justification, uncertainty against determinism and teleology against causality. It is also compared with visioning to open a window to conceive differences between back-casting and other techniques as guidance in using such techniques in practice Manuscript profile
      • Open Access Article

        2 - Forecasting of the Students’ Performance in Military Higher Education Using Artificial Neural Network Prediction Algorithm (Case study: A military organization)
        Mohammad Fallah Hamideh Reshadatjoo
        Background: One of the basic issues in a country's higher education system is the foundations of the quality of graduates’ and university students’ performance, which make up two of the seven major issues in the field of quality in higher education and is im More
        Background: One of the basic issues in a country's higher education system is the foundations of the quality of graduates’ and university students’ performance, which make up two of the seven major issues in the field of quality in higher education and is important in incorporating multiple components in improving the quality of the higher education system of each country, and any ambiguity in it, especially in military higher education, which has a higher sensitivity, will lead to irreparable consequences. Purpose: The main objective of this paper is to forecast the performance of military higher education students using the artificial neural network prediction algorithm. In addition, the main components of student performance quality have been studied. Method: In this paper, using predictive artificial neural network prediction algorithm, forecasting the quality of students' performance in three phases of learning, validation and neural network test was performed. The statistical society consists of faculty members of Shahid Sattari Air University, students and graduates of this university, as well as the members of the Office of Strategic Studies and Theoretical Research, were then interviewed using a semi-structured interview and a researcher-made questionnaire. Finally, MATLAB software was used to model the neural network. Results: Using artificial neural network algorithm, a model with accuracy of 85.5% was designed and tested. Conclusion: By using artificial neural network algorithm and modeling the quality of students' performance, we can accurately predict the quality of the graduates' performance in the Air Force organization. Manuscript profile
      • Open Access Article

        3 - Presenting a new model for ATM demand scenario
        Alireza Agha Gholizadeh Sayyar Mohamadreza Motadel Alireza Pour ebrahimi
        In today's competitive world, the ability to recognize predict customer demand is an important issue for the success of organizations. And since ATMs are one of the most important channels for cash distribution and one of the most fundamental criteria for assessing the More
        In today's competitive world, the ability to recognize predict customer demand is an important issue for the success of organizations. And since ATMs are one of the most important channels for cash distribution and one of the most fundamental criteria for assessing the level of service to banks,In this paper, the number of referrers to ATM devices is reviewed based on the timing and location of the devices. This article seeks to find a dynamic and functional model for predicting the number of referrers to each ATM depending on the time and location of the device. Hence, 378 ATM machines were used throughout the city of Tehran for a time period of one month, containing 69,418 records. Finally, with the help of clustering of statistical data in spatial and temporal dimensions, this model finally succeeds in learning the pattern in the macro data, and based on the decision tree, the predictor can predict the number of referents to each device, which after the algorithm is presented. In order to improve the quality of banking services and improve the performance of the ATM network, it is proposed to combine the optimal location of ATMs in spatial and temporal dimensions. Manuscript profile
      • Open Access Article

        4 - Technology Forecasting Based on Text Mining Patents and Cluster Analysis: Case Study Photovoltaic Technology
        Zohre Bayanloo Habib Zare Ahmadabadi
        Nowadays, solar energy has been utilized in various ways and photovoltaic technology is one of them. Photovoltaic phenomena is a phenomena in which solar energy  converts to the electrical energy “direct”. Shrinkage of fossil fuel resources as the curre More
        Nowadays, solar energy has been utilized in various ways and photovoltaic technology is one of them. Photovoltaic phenomena is a phenomena in which solar energy  converts to the electrical energy “direct”. Shrinkage of fossil fuel resources as the current main source of energy, is one of the main concerns of today’s world. Because of the solar energy has attracted attentions as an alternative for fossil fuels recently. Technology forecasting has been defined as pre-realization of promising future technology progresses and assessment of its potential. Researchers use numerous methods to forecast technology and analysis of patent is one of them. In this article international patents, related to the area, which are registered in USPTO from 1985 to 2016 has been extracted. By implementation of text mining and two step clustering approach, it turns out that there is research gaps in this sector of technology which is not considered. As the result, research gaps and future research opportunities for researchers were identified and presented. Manuscript profile
      • Open Access Article

        5 - Using an approach based on financial forecasting and soft econometrics for the future research of systems behavior
        NABI OMIDI
        10.30495/jmfr.2023.45694.1862
        Today, forecasting methods based on soft econometrics as well as financial forecasting methods are used in various systems, one of the aspects of using forecasting methods is to use it to predict the behavior of general systems and It is a quote. In this research, using More
        Today, forecasting methods based on soft econometrics as well as financial forecasting methods are used in various systems, one of the aspects of using forecasting methods is to use it to predict the behavior of general systems and It is a quote. In this research, using the statistics of traffic injuries referred to forensic medicine in Golestan province between April 1374 and March 1401, which were referred to forensic medicine in Golestan province, and using artificial neural network, which is one of the most advanced methods of forecasting and future research In the field of health systems, the number of injured people has been predicted for the 12 months ending in 1402. Also, the accuracy of this method has been measured using the average percentage of the absolute value of the error. The results of the research showed that the artificial neural network with 12 inputs, one output and 5 hidden layers is suitable for predicting the injured referred to Golestan forensic medicine,. The predicted values showed that the number of traffic injuries in Golestan province is increasing. Due to the high accuracy of the neural network in this research, this method can be used as a basis for future research in accidents. The upward trend in the number of traffic injuries in Golestan province indicates the need to review decisions in the field of transportation in this province. Manuscript profile
      • Open Access Article

        6 - A review of theoretical foundation and key concepts of futures studies regards to development of implementation framework of Futures Studies
        Ardeshir Sayah Mofazali Alireza Asadi
        Futures studies as an academic field is a new era of social science that concerns a historical issue knowing future, in scientific approach. Futures Studies as a branch of social science has its own paradigms, theories and schools of thoughts that explains future dimens More
        Futures studies as an academic field is a new era of social science that concerns a historical issue knowing future, in scientific approach. Futures Studies as a branch of social science has its own paradigms, theories and schools of thoughts that explains future dimension. During decades many methods and tools has been developed to study trends, emerging issues and events by scholars and professionals. In this paper authors are reviewing the evolution of theoretical foundations of futures studies to explain the key concepts and basis of this era; Including the development of thinking about future of civilization and utopia since the 16th century to the present, differences between prediction and futures study, alternative futures versus single predefined future, dimensions of the future, path dependencies versus future breaking, tools and methods of futures studies. In light of this review, a conceptual framework has been developed to choose appropriate strategy for futures studies research. However without deep understanding and systematic methodology of futures research, applying tools could not meet the complicated needs of societies and organization in face of emerging issues in further new world. Manuscript profile
      • Open Access Article

        7 - Expanding the Application Models Box Jenkins, Artificial Neural Network and Adjusted Exponential forecasting Social Phenomena (Case study: forecasting of marriage and divorce in Ilam)
        Mohammadreza Omidi Nabi Omidi Ardashir Shiri R. Mohammadipour
        One of the most important tools in the hands of managers and experts to make strategic decisions is Methods of forecasting and futures. Despite the development of prediction methods, but less likely to use these methods in predicting social phenomena such as marriage, More
        One of the most important tools in the hands of managers and experts to make strategic decisions is Methods of forecasting and futures. Despite the development of prediction methods, but less likely to use these methods in predicting social phenomena such as marriage, divorce and population growth are discussed. In this study, using data from marriage and divorce between the years 1992 to 2013 in Ilam province to forecasts, the number of these phenomena using models Box Jenkins, Artificial Neural Network and Adjusted Exponential has been studied for years to come. The results showed that the prediction accuracy Box Jenkins model to predict the number of marriages and Artificial Neural Network model to predict the number of divorces is more than any other prediction methods. The predicted values showed that the proportion of marriages end in divorce in Ilam province between the years 2014 to 2018 following the gentle slope, to reduce the move. Manuscript profile
      • Open Access Article

        8 - Expanding the Application Models Box Jenkins, Artificial Neural Network and Adjusted Exponential forecasting Social Phenomena (Case study: forecasting of marriage and divorce in Ilam)
        Mohammadreza Omidi Nabi Omidi Ardeshir Shiri Rahmatullah Mohammadipour
        One of the most important tools in the hands of managers and experts to make strategic decisions is Methods of forecasting and futures. Despite the development of prediction methods, but less likely to use these methods in predicting social phenomena such as marriage, d More
        One of the most important tools in the hands of managers and experts to make strategic decisions is Methods of forecasting and futures. Despite the development of prediction methods, but less likely to use these methods in predicting social phenomena such as marriage, divorce and population growth are discussed. In this study, using data from marriage and divorce between the years 1992 to 2013 in Ilam province to forecasts, the number of these phenomena using models Box Jenkins, Artificial Neural Network and Adjusted Exponential has been studied for years to come. The results showed that the prediction accuracy Box Jenkins model to predict the number of marriages and Artificial Neural Network model to predict the number of divorces is more than any other prediction methods. The predicted values showed that the proportion of marriages end in divorce in Ilam province between the years 2014 to 2018 following the gentle slope, to reduce the move.   Manuscript profile
      • Open Access Article

        9 - Financial Market Forecasting Methods under Structural Break
        Frozandeh Jafarzadehpour Amir Nazemy Alireza Asadie
        Financial market forecasting particularly stock market forecasting is a considerable debate that confront to forecast failure and model break down when structural breaks in trends occur.  This paper discusses the modeling to predict stock return under structural br More
        Financial market forecasting particularly stock market forecasting is a considerable debate that confront to forecast failure and model break down when structural breaks in trends occur.  This paper discusses the modeling to predict stock return under structural breaks and investigate new approaches of forecasting in this condition. This study proposes a taxonomy for research area in forecasting under structural breaks to suggest further studies. We use literature survey as methodology of the research and categorizes the methods, models, and results of the recent researches in stock market forecasting. Consequently, it provides three categories of strategies to forecast stock return under structural breaks. First strategy, called economically motivated model restrictions, uses financial theories as signs to adjust the parameters of models in out-sample periods. Second strategy, known as regime shift, uses a Markov chain transition matrix to model structural breaks in time series. Third strategy applies mix of quantitative models and qualitative surveys to predict future of financial markets. The proposed strategies are applicable in Tehran stock exchange under uncertainty conditions. Manuscript profile
      • Open Access Article

        10 - Modeling and forecasting electricity production and Consumption in Iran
        Mohammadreza Omidi Nabi Omidi Heshmatolah Asgari Meysam Jafari Eskandari
        Due to the relatively high growth of energy consumption in the country, the future of research in the field of electrical energy as an important intermediate inputs in industrial production and as a final good And the necessary domestic and commercial sector, the requir More
        Due to the relatively high growth of energy consumption in the country, the future of research in the field of electrical energy as an important intermediate inputs in industrial production and as a final good And the necessary domestic and commercial sector, the requirements of law enforcement agencies in the field of production and consumption of electricity. Review and forecast electricity consumption and production managers a valuable factor in the power industry for strategic decision making. In this study, using time-series production and power consumption between the years 1967-2013 and deployment of predictive models Box Jenkins, artificial neural network and gray system in addition to the forecasts for the coming years using the standard average percentage of errors the accuracy of prediction methods were also studied villages. The results showed that the highest accuracy in the prediction of Box Jenkins methods and artificial neural network to predict the power consumption is the highest accuracy. The predicted values showed a decreasing ratio of production to consumption in Iran is relatively constant desire and The electricity production in Iran in 2019 to 318 843 million kW per hour and power consumption to be 260,645 million kWh, Which can be modified using modern methods of production and consumption patterns towards increased production to consumption. Manuscript profile
      • Open Access Article

        11 - Forecasting of Banks Liquidity resources
        Dr.Ahmad Yazdanpanah Zahra Abbasi
        Liquidity management is one of the most important functions of financialmanagement in economic firms. In the case of financial and creditinstitutions especially banks, it has a more important role. Banks requireto maintain a portion of their assets in the form of cash i More
        Liquidity management is one of the most important functions of financialmanagement in economic firms. In the case of financial and creditinstitutions especially banks, it has a more important role. Banks requireto maintain a portion of their assets in the form of cash in order to be ableto respond to their customer’s needs. However, it has an opportunity costfor the bank. In other words, keeping cash in current accounts ormaintaining it by Central Bank or other banks decreases the risk of bankliquidity while it deprives banks of investment opportunities and declinesthe bank returns.In this study, therefore, we tried to design a model in order to forecast thecash amounts of EN-Bank kept in current accounts or maintained byCentral Bank or other banks which is totally called “Bank Liquidity”.Thus, forecast was done based on input cash flow during a specificperiod. Then by comparing this with the goals and strategies of the bank,it has been planned to eliminate the budget deficit or surplus consumptionin order to reach the equilibrium at the end of the period. In this method,current accounts, interbank accounts and funds are considered asliquidity. ARIMA and Minitab software are used in order to estimate themodel.At the end, forecast was done for the next 52 weeks by this model. As aresult, it was observed that bank will be faced surplus liquidity Manuscript profile
      • Open Access Article

        12 - Forecasting the Price of Natural Gas Using Developed Methods Based on Grays and Fractals
        Saeed Emami Koupaee Shiva Zamani A. Reza Heidarzadeh Hanzaee M. Reza Shahnazari
        The importance of predicting the price of energy carriers for the development of the economy and industry today is not overlooked. Meanwhile, predicting natural gas prices as one of the most important carriers of energy and an important role in providing clean energy ca More
        The importance of predicting the price of energy carriers for the development of the economy and industry today is not overlooked. Meanwhile, predicting natural gas prices as one of the most important carriers of energy and an important role in providing clean energy can be considered as an important tool in industrial development decision making. In this paper, we have investigated the nonlinear behavior of natural gas prices in a multi-year period, as well we have introduced methods for the development and synthesis of fractalization (FDGM) has been used to predict the price of natural gas. The results of the price forecast based on the introduced methods, Indicates the effectiveness of these methods. At the same time, given the fractal nature of the price of natural gas in the period under review, the results show that the forecast error using the FDGM method is always below 7%. And very good results were obtained using combination fractional and fractional methods. Manuscript profile
      • Open Access Article

        13 - Combined Application of State Space in ARIMA Form Model and Monte Carlo Simulation Method to Forecast TEPIX Index
        Aghigh Farhadi Farhad Ghaffari
        In this study, we estimated the parameters using the State Space model described inARIMA form. We’ve also used the Monte Carlo Method for simulating the process in10000 reputations. Then the estimated parameters and the Monte Carlo simulationmethod are used to for More
        In this study, we estimated the parameters using the State Space model described inARIMA form. We’ve also used the Monte Carlo Method for simulating the process in10000 reputations. Then the estimated parameters and the Monte Carlo simulationmethod are used to forecast TEPIX index, including 739 observations as an in-sampledata from 21th of January 2011 to 19th February 2014 and 59 observations from 20thFebruary 2014 to 21th May 2014 as an out of sample data . Furthermore, For moreinvestigation we’ve considered different horizons of forecasting, short-term (equal to 1week), mid-term (equal to 1 month) and long term (equal to 3 month). The results showedthat Tehran stock market data has enough efficiency to forecast them, and showed that theState Space in Form ARIMA model and the Monte Carlo simulation method can be usedas a predictive algorithm for TEPIX index and other indices with similar nature. Manuscript profile
      • Open Access Article

        14 - Investigation of Multifractaly Models in Finance
        Fraydoon R. Roodposhti Mahdeyeh Klantari Dehaghi
        Specifying the governing process of stock market’s return with the goal of making optimal decisions and reducing the risk cost has a great importance for investors and policy makers. The importance of market analysis on one hand and the effort for comprehending th More
        Specifying the governing process of stock market’s return with the goal of making optimal decisions and reducing the risk cost has a great importance for investors and policy makers. The importance of market analysis on one hand and the effort for comprehending the markets on the other hand resulted in that, after the assumptions of efficient market were challenged and universal financial facts such as “fat tails” and “volatility clustering” were discovered, analysts leaned toward multifractaly and Lévy models and moved away from models with random characteristics and normal distribution. This caused multifractal models to be used in different segments of the market. In this article the multifractal approach that in recent years has been used for forecasting and modeling volatility, will be examined. In the beginning the origin of this approach that stems from turbulent flows in statistic physic will be introduced and in the following sections the details about the specifications and features of multifractal time series models, available approaches for estimating them and empirical uses of this model will be mentioned. The results of this research show that the dynamic nature of capital market has caused the approaches, methods and models of market analysis to be permanently changing. In addition, for volatility clustering of time series, smaller scales are considered. Manuscript profile
      • Open Access Article

        15 - Investigation of Volatility Forecast Errors using Geometric Brownian Motion and GARCH Models in Sector Indices of Tehran Securities Exchange
        Ershad Emami Alireza Heidarzadeh Hanzaei
        10.30495/jfksa.2022.21084
        Current study compares forecasting capability of GARCH (1,1) against Geometric Brownian Motion, GBM, model for daily volatility of indices. The question is to study whether accuracy of GBM forecast differ significantly from its comparing model. Our data consists of 5.5 More
        Current study compares forecasting capability of GARCH (1,1) against Geometric Brownian Motion, GBM, model for daily volatility of indices. The question is to study whether accuracy of GBM forecast differ significantly from its comparing model. Our data consists of 5.5 years (2015 – 2019) of daily logarithmic returns from 38 sector indices within Tehran Stock Exchange. The data was split into estimation period (5 years of daily data) and forecast period (daily data of the remaining 6 months). The competing models were estimated using maximum likelihood method and based on moving window approach, in which the length of estimating period was kept fixed, and projections were conducted on a daily basis. Root Mean Square Error, RMSE, approach was employed to measure forecasting error of each model. The one with less error will express more capability in forecasting daily volatility. With only three instances of a precise forecast, GARCH showed a relatively worse performance, in comparison to GBM.. Manuscript profile
      • Open Access Article

        16 - Identifying Banking Crisis Using Banking Stress Index in Iranian Economy (Dynamic Factor Model)
        samineh ghasemifar Abolfazl Shahabadi shamsollah shirinbakhsh mirhosien mousavi azam ahmadian
        By the fact that most of the public and private sector financing comes from the country's banking sector, It is important to maintain stability and prevent a crisis in the banking system. The purpose of this study is to identify the banking crisis using the Banking Stre More
        By the fact that most of the public and private sector financing comes from the country's banking sector, It is important to maintain stability and prevent a crisis in the banking system. The purpose of this study is to identify the banking crisis using the Banking Stress Index in the Iranian economy for the period of 1398-1388. The Banking Stress Index is the best benchmark for assessing the banking crisis that reflects uncertainty, instability and financial friction in the banking system. In this study, the design of a bank stress index was performed using a dynamic factor model. This model is estimated by the maximum likelihood method and the stochastic pattern of missing data. Using six variables determining the banking crisis in the country, two banking stress indices with two different natures have been estimated in time series to examine the stability of the banking system. Finally, both indices of stress showed estimation; there is a precise timing of the coincidence between the greatest amounts of bank stress and the shocks to the Iranian economy. It was also concluded that bank stress indicators reflect the effects of external factors, including sanctions on the banking system fundamental weaknesses of the banking system, as well as being able to predict banking crises Manuscript profile
      • Open Access Article

        17 - Forecasting Petroleum Futures Markets Volatility with GARCH and Markov Regime-Switching GARCH Models
        مرتضی بکی حسکوئی فاطمه خواجوند
        In this paper we compare a set of different standard GARCH models with a group ofMarkov Regime-Switching GARCH (MRS-GARCH) in terms of their ability to forecastthe petroleum futures markets volatility at horizons that range from one day to onemonth. To take into account More
        In this paper we compare a set of different standard GARCH models with a group ofMarkov Regime-Switching GARCH (MRS-GARCH) in terms of their ability to forecastthe petroleum futures markets volatility at horizons that range from one day to onemonth. To take into account the excessive persistence usually found in GARCH modelsthat implies too smooth and too high volatility forecasts, MRS-GARCH models, wherethe parameters are allowed to switch between a low and a high volatility regime, areanalyzed. Both gaussian and fat-tailed conditional distributions for the residuals areassumed, and the degrees of freedom can also be state-dependent to capture possibletime-varying kurtosis. The forecasting performances of the competing models areevaluated with statistical loss functions. Under statistical losses, we use both tests ofequal predictive ability of the Diebold-Mariano-type and test of superior predictiveability, such as White􀀀s Reality Check and Hansen􀀀s SPA test. The empirical analysisdemonstrates that MRS-GARCH models do really outperform all standard GARCHmodels in forecasting volatility at shorter horizons according to a broad set of statisticalloss functions. At longer horizons standard asymmetric GARCH models fare the best.All this tests reject the presence of a better model than the MRS-GARCH-t in thisresearch Manuscript profile
      • Open Access Article

        18 - Long memory investigation and application of wavelet decomposition to improve the performance of stock market volatility forecasting
        شمس اله شیرین بخش اسماعیل نادری نادیا گندلی علیخانی
        Because of very large frequency and volatility in Financial markets Indicators, acertain type of non stationary is created that it refers to the fraction non stationary. Thiscauses, provides Long memory in this type of time series. Hence, this study has inaddition to ex More
        Because of very large frequency and volatility in Financial markets Indicators, acertain type of non stationary is created that it refers to the fraction non stationary. Thiscauses, provides Long memory in this type of time series. Hence, this study has inaddition to examine the existence of the long memory in both mean and varianceequations in the return series of Tehran stock exchange, Pays to forecasting the volatilityof this index. For this purpose, the daily data from fifth Farvardin 1388 to eighteenthOrdibehesht 1391 is used. Our results confirm the existence of Long Memory in bothmean and variance equations. However, among others, based on the information criteriaand MSE, ARFIMA (1,2)-FIGARCH(BBM) model has been selected as the bestspecification to model and forecast the volatility of Tehran stock exchange’s return. Aswell, in order to Forecasting the volatility of this series, was used Combination of theabove model with Level and decomposed data. The results show that, according to theforecasting error criteria (MSE and RMSE), the result of model’s based on decomposeddata (with wavelet technique), more acceptable. Manuscript profile
      • Open Access Article

        19 - Capability Comparison of the Models based on Long Memory and Dynamic Neural Network Models in Forecasting the Stock Return Index in Tehran Stock Exchange
        اکبر کمیجانی اسماعیل نادری
        The aim of this study is to introduce an efficient nonlinear model for predicting thereturn of Tehran Stock Exchange (TSE) Price index. For this purpose, the daily timeseries of price index from Farvardin 1388 to Aban 1390 is used. This study includes616 observations; 9 More
        The aim of this study is to introduce an efficient nonlinear model for predicting thereturn of Tehran Stock Exchange (TSE) Price index. For this purpose, the daily timeseries of price index from Farvardin 1388 to Aban 1390 is used. This study includes616 observations; 90% of which used for estimating coefficients and the remaining 60observation are deduced for out of sample forecasting. By comparing the results of anonlinear dynamic artificial neural network (NNAR) and a nonlinear regression model(autoregressive fractional integration moving average «ARFIMA»), we found thatNNAR models have better performance in out of sample forecasting based on meansquare error criteria (MSE) and root mean square error criteria (RMSE) than thenonlinear regression models (ARFIMA). Manuscript profile
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        20 - The Predication of Stock Price Using Firely Algorithm
        Ali Bayat Zeynab Bagheri
        In this study, the prediction of stock price of some manufactors listed in Tehran stock market and some others, using firefly algorithm has be done.In this study firstly, we used 16 variables for a period of 3 years (1388-1392) to educating the algorithm and after that More
        In this study, the prediction of stock price of some manufactors listed in Tehran stock market and some others, using firefly algorithm has be done.In this study firstly, we used 16 variables for a period of 3 years (1388-1392) to educating the algorithm and after that , we used educated algorithm to predict the stock price of manufactors with 12 variables. the relative fault was calculated for stock prices for before and after prediction. the average of This fault isless than %6 and the result is that the stock price prediction using fire fly algorithm is achievable and possible. Manuscript profile
      • Open Access Article

        21 - The Proposed Model For Prediction Of GDP Using With ARIMA, Neural Networks And Wavelet Transform
        bita Shaygani Amir behdad Salami Ramin Khochiani
        Forecasting GDP, is one of the most important economic issues and due to its practical applications has attracted a lot of attentions. Methods of time series analysis and nonlinear methods such as neural network models as long as are used to forecast such variables . GD More
        Forecasting GDP, is one of the most important economic issues and due to its practical applications has attracted a lot of attentions. Methods of time series analysis and nonlinear methods such as neural network models as long as are used to forecast such variables . GDP's time series is variable that after the decomposition, with wavelet - a powerful tool for processing data- and analyzing the hidden layers, at some levels, has linear behavior and at other levels, has nonlinear behavior.Therefore, the proposed method would be thus that the time series of quarterly GDP for the period 1367 to 1389 using wavelet techniques are decomposed into different scale components. Next, the approximation level (trend) and cycles with linear behavior have predicted with ARIMA model, and cycles with the nonlinear behavior have predicted with neural network model.The results show that the performance of the proposed method is better than the neural network (NARNET) and ARIMA models. Manuscript profile
      • Open Access Article

        22 - Proposing a synthetic approach (FARIMA) by employing ARIMA and fuzzy regression methods in order to forecast crude oil price
        قدرت الله امام وردی مریم شهابی طبری
        The ARIMA model is a precise forecasting model for short time periods, but the limitation of a large amount of historical data is required. However, in our society, due to uncertainty and rapid development of new technology, we usually have to forecast future situations More
        The ARIMA model is a precise forecasting model for short time periods, but the limitation of a large amount of historical data is required. However, in our society, due to uncertainty and rapid development of new technology, we usually have to forecast future situations using little data in a short span of time. The historical data must be less than what the ARIMA model employs which limits its application. The fuzzy regression is able to forecast model which is suitable for the uncertain condition and with little attainable historical data. But the results of this model cannot be encouraging because the spread is wide in some cases. The researchers do try to combine the advantages of the fuzzy regression and ARIMA models to formulate the FARIMA model and to overcome the limitations of the fuzzy regression and ARIMA model. Therefore, in this study, a synthetic fuzzy auto regressive integrated moving average (FARIMA) is employed to forecast crude oil price. The findings show that the proposed method can get more satisfactory results. Manuscript profile
      • Open Access Article

        23 - Comparison of Models in Predicting Cumulative Cases of Hospitalization and Death of Covid-19 (Case Study: Bahabad city
        mohammad hossein karimizarchi Davood Shishebori
        10.30495/jhm.2023.72246.11112
        Introduction: Coronavirus disease 2019 is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths during todays can be a useful step in predicting the costs and facilities needed in the future. This study More
        Introduction: Coronavirus disease 2019 is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths during todays can be a useful step in predicting the costs and facilities needed in the future. This study aims to modeling, comparing the performance of models, and predict new cases and deaths efficiently in the future. Methods: In this article nine popular forecasting techniques are tested on the data of Covid-19 in Bahabad city as a case study. Using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and the mean absolute percentage of error (MAPE) of the models are compared.  Results: The results of the analysis showed that the best model according to the evaluation criteria for forecasting cumulative cases of hospitalization of Covid-19 is the cubic spline smoothing model, and cumulative cases of death, KNN regression model. Also, autoregressive neural network and theta models for hospitalization cases, and for death cases, autoregressive neural network model has the worst performance among other models. Conclusion: This study can provide a proper understanding of the spread of covid-19 disease in this region so that by taking precautionary measures and formulating appropriate policies, this epidemic can be effectively overcome. Also, unlike other studies, this study uses 9 different techniques and their comparison, which in turn increases the confidence factor in decision making. Also, an important point is that the data should be updated in real time. Manuscript profile
      • Open Access Article

        24 - The Impact of Accounting Conservatism on Earnings Management Forecasting Error
        Ahmad Lotfi MEISAM HAJI POR
        Earnings is one of the important and essential components of financial statementsthat is of particular interest to financial statements users. The information which isissued with a company and as a results earnings , is based on past events but investorsneed information More
        Earnings is one of the important and essential components of financial statementsthat is of particular interest to financial statements users. The information which isissued with a company and as a results earnings , is based on past events but investorsneed information about firm’s future. Firm’s Managements who have enoughinformation and resource, hasten the efficiency of financial market by earningsforecast . Prior researches has indicated that managements forecasts have effect onstock price, stock markets liquidity and analysts forecasts . On the other hand, theaccuracy or error of predicted earnings is affected by size, age , structure of companyand etc . Also it’s confirmed that managements psychological bias has impact ontheir forecasts . further empirical researches show that accounting policies areconservative and become more conservative from thirty past years (Watts,2003).Basu(1997) and following him found evidence of accounting conservatismdevelopment. Li(2007), also, find that in times of growth in investment, accountingconservatism leads to a downward bias in reported earnings and net assets. In thispaper, we examine the effect of accounting conservatism on management earningsforecasting error. Empirical findings are according to a sample from 88 companies ofTehran stock exchange during the period of 1998-2008 support our hypotheses. Inother words companies which have more conservative policy have less earningsforecasting error . Manuscript profile
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        25 - Comparative between cost prediction using statistical methods and neural networks
        امیر محمدزاده نسرین مهدی پور آرش محمدزاده
        Prediction of total cost of water helps the Isfahan municipality to optimize thewater usage in its 14 urban zone. The total cost of water, basically, depends ondifferent parameters. Generally, the analytically prediction of the total cost is verydifficult if not impossi More
        Prediction of total cost of water helps the Isfahan municipality to optimize thewater usage in its 14 urban zone. The total cost of water, basically, depends ondifferent parameters. Generally, the analytically prediction of the total cost is verydifficult if not impossible. Thus, applying intelligent systems such as neural networkmodels can be a good alternative. In this paper, using multi-layer perceptron neuralnetwork and error back propagation algorithm, the total cost of municipal water in theIsfahan municipality is calculated based on parameters such as per capita populationand area of each urban zone. In this paper, a model for simulation and prediction ofthe annual total cost of water in Isfahan municipality is developed. The simulationmodel is developed using the regression and the neural network model is built usingdata from 2004 to 2009. Finally, the neural network method is selected as the mainsimulation method for forecasting the total cost of water in the 14 urban zones ofIsfahan. Manuscript profile
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        26 - The presentation of energy models in programs of socio-economic development
        Mohammadreza Moghaddam
        Manuscript profile
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        27 - Analysis and forecasting of precipitation in the Larestan area by Markov chain.
        بهلول Alijani زین العابدین Jafarpoor حیدر Ghaderi
        In order to analyze the precipitation of the Larestan area, the rain days with 0.1millimeter or more were obtained from the Iranian Meteorological Organization for the1960-2003 period. First the rainy periods with different lengths were identified andtheir monthly and s More
        In order to analyze the precipitation of the Larestan area, the rain days with 0.1millimeter or more were obtained from the Iranian Meteorological Organization for the1960-2003 period. First the rainy periods with different lengths were identified andtheir monthly and seasonal frequencies were calculated. On the monthly basis Januaryhad the highest wet days frequency and winter was the wettest but the spring was thedriest season. The wettest year had 44 rain days while only 11 days were experiencedduring the dry year. The mean daily density of rain was 8.2 mm and the mean timeinterval between successive rainy periods was 6.2 days. On the average the rainyperiod begins each year on 8 of December and ends on 6 of April.The first order Markov chain was applied to the data series to forecast the wetperiods. The model responded well and was able to forecast significantly andprecisely. The model was fitted best for the runs of one to six days proving thehypothesis of the study. Manuscript profile
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        28 - The analysis and forecasting of climatic fluctuation of khorasan
        Alireza Banivaheb
        Concerning the drought being experienced recently and its effect on planning, and economical, agricultural fields and the demands on predicting and applying different models for decision making, Markov Chains model was applied in Khorasan at Mashhad , Torbat Heydarieh , More
        Concerning the drought being experienced recently and its effect on planning, and economical, agricultural fields and the demands on predicting and applying different models for decision making, Markov Chains model was applied in Khorasan at Mashhad , Torbat Heydarieh , Birjand and Bojnord stations . This model studies the phenomena which depend on the previous ones. Here, we have studied the possibility of the occurrence of dry and wet days ( wetness threshold of 0.1 mm) , the cold days   ( below ) and warm days ( above 25C) . Finally , two analyses were done using Markov Chains model. Also, for predicting 1 to 10 day  periods , Xn=Pn-1 × q from statistical distribution was applied and the data was presented in the form of equiprobable maps . To analyze the provided data and maps , SPSS and SURFER softwares were applied . Manuscript profile
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        29 - A two-stage study of grey system theory and DEA in strategic alliance: An application in Vietnamese Steel industry
        Hoang-Phu Nguyen Nhu-Ty Nguyen
        Manuscript profile
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        30 - Drought Forecasting Using Wavelet - Support Vector Machine and Standardized Precipitation Index (Case Study: Urmia Lake-Iran)
        Mehdi Komasi Soroush Sharghi
        10.22034/jest.2020.9578
        Background and Objectives: Drought is regarded as a serious threat for people and environment. As a result, finding some indices to forecast the drought is an important issue that needs to be addressed urgently. The appropriate and flexible index for drought classificat More
        Background and Objectives: Drought is regarded as a serious threat for people and environment. As a result, finding some indices to forecast the drought is an important issue that needs to be addressed urgently. The appropriate and flexible index for drought classification is the Standardized Precipitation Index (SPI). Artificial intelligence models were commonly used to forecast SPI time series. These models are based on auto regressive property. So, they are not able to monitor the seasonal and long-term patterns in time series. In this study, the Wavelet-Support Vector Machine (WSVM) approach was used for the drought forecasting through employing SPI. Method: In this way, the SPI time series of Urmia Lake watershed was decomposed to multiple frequent time series by wavelet transform; then, these time series were imposed as input data to the Support Vector Machine (SVM) model to forecast the drought. Findings: The results showed that, the maximum value of R2 and minimum value of RMSE indexes for SVM model are 0.865 and 0.237 and for WSVM model are 0.954 and 0.056 respectively in verification step. Discussion and Conclusion: So, the propounded hybrid model has superior ability in forecasting SPI time series comparing with the single SVM model and also it can accurately assess the extreme data in SPI time series by considering the seasonality effects. Finally, it was concluded that, the proposed hybrid model is relatively more appropriate than classical autoregressive models such as ANN.   Manuscript profile
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        31 - Forecasting Municipal Solid Waste Quantity by Intelligent Models and Their Uncertainty Analysis
        Maryam Abbasi Malihe Fallah Nezhad Rooholah Noori Maryam Mirabi
        10.30495/jest.2020.11144
        Background and Objective: The first step in design of municipal waste management systems is complete understanding of waste generation quantity. Forecasting waste generation is one of the most complex engineering problems due to the effect of various and out of control More
        Background and Objective: The first step in design of municipal waste management systems is complete understanding of waste generation quantity. Forecasting waste generation is one of the most complex engineering problems due to the effect of various and out of control parameters on waste generation. Therefore, it is obvious that it is necessary to develop approaches to a model such complex events. The objective of this study is forecasting waste generation quantity using intelligent models as well as their comparisons and uncertainty analysis.Method: In this study, Mashhad city was selected as a case study and waste generation time series of waste generation in 1380 to 1390 were used for weekly prediction. Intelligent models including artificial neural network, support vector machine, adaptive neuro-fuzzy inference system as well as K-nearest neighbors were used for modelling. After optimizing the models’ parameters, models’ accuracy were compared by statistical indices. Finally, result uncertainty of the models was done by Mont Carlo technique.Findings: Results showed that coefficient of determination (R2) of artificial neural network adaptive neuro-fuzzy inference system, support vector machine, and K-nearest neighbor models were 0.67, 0.69, 0.72 and 0.64 respectively. Uncertainty analysis was also justified the results and demonstrates that support vector machine model had the lowest uncertainty among other models and the lowest sensitivity to input variables.Conclusion: Intelligent models were successfully able to forecast waste quantity and among the studied models, support vector machine was the best predictive model. Moreover, support vector machine produced the results with the lowest uncertainty the other models. Manuscript profile
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        32 - Investigating the Role of Global Warming on Wind Speed and Sea Level Pressure Fluctuations in Sistan Region
        Esmaeil Poudineh Broumand Salahi Mahmoud Khosravi Mohsen Hamidianpour
        10.22034/jest.2019.29479.3806
        Background and Objective: The temporal variability of local winds of Sistan during the period of global warming is the subject of this research. In recent decades, global warming has brought about tangible changes in the temperature of the planet and has influenced othe More
        Background and Objective: The temporal variability of local winds of Sistan during the period of global warming is the subject of this research. In recent decades, global warming has brought about tangible changes in the temperature of the planet and has influenced other atmospheric parameters such as wind speed. Method: In the study of atmospheric parameters, estimating the effect of global warming on these parameters is important. For this purpose, variations in the Sistan wind speeds and sea level pressure in the study area under the conditions of the two scenarios A2 and B2 from the output of the global Hadcm3 model were downscaled and for three periods of 30 years up to 2099, the changes in these two parameters were generated and examined. Findings: The results showed that the average wind speed calculated by scenario B2 for the period 2010-2039, 2040-2069 and 2070-2070 respectively 0.67, 0.88 and 1.15 m / s Relative to the Basic course will increase. Also, the average wind speed variation under A2 scenario Conditions, which is a pessimistic scenario, is 1.36 and 1. 57 and 1.79 m / s for the periods 2039-2039 and 2069-2070 and 2070-2070 Also, the pressure calculated by scenario B2 for the period 2010-2039, 2040-2069, and 2070-2070 will be reduced to 0.04, 0.10, and 0.16, respectively, compared to the base period. Discussion and Conclusions:  The results showed that the decline in pressure and increase in wind speed has not been uniformly distributed throughout the year. However, during the winter and spring and summer, pressure drop is more regular than the autumn season. Manuscript profile
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        33 - Uncertainty Evaluation of ANN and ANFIS Models in Inflow Forecasting into the Raees-Ali Delvari Dam
        Ali Eskandari Roohollah Noori Mohammad Reza Vesali Naseh Farimah Saeedi
        10.22034/jest.2020.20068.2909
        Background and Objective: Accurate information about the river flow significantly influences the water resources management for the communities that use the water. In this regard, this study aims to present a reliable prediction of the monthly discharge of Shahpour Rive More
        Background and Objective: Accurate information about the river flow significantly influences the water resources management for the communities that use the water. In this regard, this study aims to present a reliable prediction of the monthly discharge of Shahpour River, inflow to Raees-Ali Delvari Dam, located in the Boushehr Province, Iran. Methods: To forecast the monthly inflow to Raees-Ali Delvari Dam, the artificial intelligence models, i.e. artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), were applied. Also, uncertainty determination of the both models was carried out in order to improve the application of their results in the management decisions in the water sector. In this regard, the simulated results of the models, tuned with the different pattern of calibration data, were used. Two indices, i.e. the width of confidence band (d-factor) and the values bracketed by 95 percent prediction uncertainties (95PPU) were applied in order to evaluate the models’ uncertainty.  Findings: Results of tuned ANN and ANFIS models indicated that although the both models had the appropriate values of determination coefficient (R2) and mean absolute error (MAE), their performance was along with considerable errors in the high extreme values. Besides, a look at through the uncertainty results of the models indicated the ANFIS model, that included the less d-factor and higher 95PPU values, had less uncertainty than the ANN. Discussion and Conclusion: Considering the same performance of the both ANN and ANFIS models in the calibration and test steps, it can be concluded that the ANFIS model was the best selection for monthly inflow prediction into Raees-Ali Delvari Dam due to its less uncertainty that ANN model. Manuscript profile
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        34 - Comparison of Autoregressive Static and Artificial Dynamic Neural Network for the Forecasting of Monthly Inflow of Dez Reservoir
        Mohammad Ebrahim Banihabib Mohammad Valipoor S. Mahmood Behbahani
        In this paper, the capability of autoregressive static and artificial dynamic neural networks models was compared for forecasting of monthly inflow of Dez reservoir. In previous researches, static and artificial dynamic neural networks models have not been compared More
        In this paper, the capability of autoregressive static and artificial dynamic neural networks models was compared for forecasting of monthly inflow of Dez reservoir. In previous researches, static and artificial dynamic neural networks models have not been compared for above-mentioned propose. In addition, using artificial neural network model as an autoregressive model is innovation point of this research.  Monthly flow data of Dez station in Dez River in years1955 to 2001 is used in this research. Data of 42 former years and 5 recent years are used for Training and testing data set, respectively. Different structure for the static and artificial dynamic neural network models were evaluated by comparing the root-mean-square error (RMSE) of the models. First, static and artificial dynamic neural network models were selected in training phase using data from October 1955 to September 1997. Then, using the selected structures, the monthly forecasted inflow of reservoir was compared with observed data from October 1997 to September 2001. Also, two types of radial and sigmoid activation function and the number of neurons in the hidden layer were investigated in this study. Results showed that the best model to forecast the reservoir inflow is autoregressive artificial neural network model associated with the sigmoid activation function and 17 neurons in the hidden layers. Artificial dynamic neural network model with sigmoid activation function can forecast reservoir inflow for 5 years better than static artificial neural networks model Manuscript profile
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        35 - Modeling and Forecasting Air Pollution of Tehran Application of Autoregressive Model with Long Memory Properties
        reza akhbari Hamid Amadeh
        10.22034/jest.2018.12462
        Background and Objective: Environmental pollution modeling is one of the essential requirements in the field of air quality monitoring which with using the output of the model, improvement of future situation can be possible. The existing literature of the modeling of e More
        Background and Objective: Environmental pollution modeling is one of the essential requirements in the field of air quality monitoring which with using the output of the model, improvement of future situation can be possible. The existing literature of the modeling of environmental pollution –especially air pollutants- could be divided to two whole categories. First, those researches that in addition of pollutants data, they used some factors such as temperature, wind direction, wind speed and humidity. The second one –which this study belong to- with using time series regression models and by usage of the existing data about each pollutant, the future situation was forecasted. Method: In this study, we forecast future pollutants (CO,PM10,NO2,SO2,O3,PM2.5) status with ARIMA, ARFIMA and ARIMA-GARCH models with Box-Jenkins approach, then the best model is determined with MSE, RMSE, MAE and MAPE. Findings: Results indicate that the assumption of existence of long-memory is acceptable but the hypothesis that always ARFIMA models prepare the best forecast is rejected. Discussion and Conclusion: This study proves the application of econometric models to predict the pollutants state. Based on the high social costs of pollutant emissions, it is recommended that using these models, identify the pollutants affecting the future of the city and reduce the level of their dissemination of efficiency plans.   Manuscript profile
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        36 - Forecasting the Effect of Decreasing Long Time Trend of Caspian Sea Water Level on the Life of Gorgan Bay
        Saeed Sharbaty Abdolazim Ghanghermeh
        Introduction: Gorgan Bay is a semi enclosed water body which currently has only a permanent connection with the Caspian Sea through mouth of Ashouradeh-Bandartorkaman. Decreasing trend of the Caspian Sea water level in past 19 years caused to adverse effects by land pro More
        Introduction: Gorgan Bay is a semi enclosed water body which currently has only a permanent connection with the Caspian Sea through mouth of Ashouradeh-Bandartorkaman. Decreasing trend of the Caspian Sea water level in past 19 years caused to adverse effects by land production in shallow coasts of Gorgan Bay and it is threatening  that limited connection of Gorgan Bay with the Caspian Sea will cause disconnection totally. Material and Methods: In this research, the effects of decreasing trend of the Caspian Sea water level on the connection status of Gorgan Bay were modeled under two scenarios of decreasing mean water level collections of the Caspian Sea. In first set of scenarios, the average of 5.2 cm and in set of second scenarios the average of 10.5 cm of decreasing water level in the Caspian Sea were used to topography position modeling in the Gorgan Bay. Results and Discussion: Under the first set of scenarios, the results of modeling show that, the connection of the Gorgan Bay with the Caspian Sea will be cut off permanently in Chapaghly area affected by decreasing in negative level in 27.6 meter in water year of 1410 – 1411. Under the second set of scenarios, the results of modeling show that the connection of the Gorgan Bay with the Caspian Sea will be cut off permanently in Chapaghly area affected by decreasing in negative level in 27.6 meter in water year of 1402 – 1403. Therefore it suggests that all the projects and the strategic programs of the government in southern coasts of the Caspian Sea including Gorgan Bay, engineering and operational program to be operated according to Amenagement Territoire scheme regarding to the approach of decreasing the level of the Caspian Sea and probable scenarios Manuscript profile
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        37 - Developing an Optimal Method for Financial Distress Prediction of the Firms (Case Study: Tehran Stock Exchange)
        Mansour Soufi Mahdi Homayounfar Mehdi Fadaei
        One of the most important issues in the field of financial management is how the investors distinguish between favorable investment opportunities and undesirable ones. One of the ways to help investors is to provide financial distress prediction models. According to the More
        One of the most important issues in the field of financial management is how the investors distinguish between favorable investment opportunities and undesirable ones. One of the ways to help investors is to provide financial distress prediction models. According to the various studies have been made to develop these type of models, in this study the combination of artificial neural networks (ANN) and genetic algorithm (GA) techniques based on Zimensky prediction ratios is used for modeling financial distress. The research statistical population includes public companies in Tehran stock exchange which admitted between October 2013 to October 2015 and among them 66 distressed and 150 going concern companies were selected as the research sample using screening method. The results indicate that the power of both artificial neural network and genetic algorithm models in financial distress prediction are equal (95 percent), however, the prediction error of neural network is relatively low compared to genetic algorithm. Manuscript profile
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        38 - Predicting Customer Churn in the Insurance Industry: Identifying the Influential Factors
        samaneh soltani Lifshagerd Kambiz Shahroodi Ebrahim Chirani
        Iran insurance industry has recently faced with various problems regarding fluctuations in profitability, portfolio composition, the rate of loss, the rate of penetration, retention and satisfaction of insurers and market share, due to presence of numerous insurance com More
        Iran insurance industry has recently faced with various problems regarding fluctuations in profitability, portfolio composition, the rate of loss, the rate of penetration, retention and satisfaction of insurers and market share, due to presence of numerous insurance companies in the competitive market. As a result, insurer maintenance has become a major goal for most of the insurance companies. Since in the insurance industry, like many other industries, the cost of searching for new insurers is far more expensive than retaining the current insurers, it is essential to identify the factors that drive insurers to churn. The purpose of this study is to investigate the literature and research background in the field of customer churn, which ultimately leads to identifying and classifying “influential factors in predicting customer churn in the insurance industry”. A systematic literature review method is used to collect and review previous studies by integrating automated and manual search strategies of all the related research articles in this field, published for the period 1389 to 1398 for Persian articles and 2010 to 2018 for English articles. The research findings identified 85 factors that affect customer churn, specifically in the insurance industry. They are classified into four categories; the factors related to the insurer, the factors related to the insuree, product/service related factors and factors regarding the relationship between the insurer and the insuree. Manuscript profile
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        39 - The effect of size and intensity of price jumps on forecasting index volatility in Tehran Stock Exchange
        mohsen rajab boloukat ali baghani Ali Najafi Moghadam fatemeh sarraf norouz noorolahzadeh
        It is very important to distinguish how the volatility in the return of assets occur. For this reason, in recent years, realized volatility and frequencies of daily volatility recognition studies have been developed. This study uses stock prices of 30 big companies of T More
        It is very important to distinguish how the volatility in the return of assets occur. For this reason, in recent years, realized volatility and frequencies of daily volatility recognition studies have been developed. This study uses stock prices of 30 big companies of Tehran Stock Exchange during the years 1390 (2011) to 1394 (2016) and calculates the realized stock volatility during trading days using the HAR-CJ model to examine the effect of size and intensity of price jumps in predicting index volatility. The results showed that the development of HAR-CJ and HAR-RV-CJ models using the size and intensity of jump did not have a significant effect on improving the index volatility prediction but, to a small extent, the model prediction performance Adjusts for index volatility. Also, using intraday jumps instead of daily jumps, does not improve the performance of the prediction model. Manuscript profile
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        40 - Design and explanation of stock price forecasting model in the real estate companies's stock in the Tehran Stock Exchange using Stochastic Process
        hossein ojaghi Zadaleh Fathi Mehrzad Minouie
        The present study has designed and explained the stock price forecasting model using stochastic processes. The statistical population of the study is all companies of the mass real estate industry in the Tehran Stock Exchange from 1390 to 1398. Data were analyzed in Evi More
        The present study has designed and explained the stock price forecasting model using stochastic processes. The statistical population of the study is all companies of the mass real estate industry in the Tehran Stock Exchange from 1390 to 1398. Data were analyzed in Eviews10 and MATLAB software. Predicting stock price behavior and the whole industry index by autoregressive methods and moving average in terms of random processes showed that the explained pattern can not be used to predict stock price behavior but in some random steps, the forecasting error was negligible. Regarding the forecast of stock price behavior, the last three steps of the process, winter have a significant effect on stock price forecast; But the first step has a significant effect on predicting the behavior of the industry index. In the first steps, the error of predicting the behavior of the industry index is very small and the explained model can be used to predict the behavior of the index in the first months of the year. Manuscript profile
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        41 - عنوان مقاله / English Daily Stock Price Movement Prediction Using Sentiment text mining of social network and data mining of Technical indicators
        Kamel Ebrahimian ebrahim abbasi Akbar Alam tabriz Amir Mohammadzadeh
        This study predicts the future movement of stock prices in the short term by using the analysis of investors' opinions on the social network. The predictability of stock markets, due to having a complex, dynamic and nonlinear system that it has always been one of the ch More
        This study predicts the future movement of stock prices in the short term by using the analysis of investors' opinions on the social network. The predictability of stock markets, due to having a complex, dynamic and nonlinear system that it has always been one of the challenges for researchers. In this research, for the first time, we developed a model with 72.08%accuracy for predicting stock movement and predicting the trend by analyzing the feelings of users' opinions and combining it with 20 technical indicators and we use three data mining algorithms include decision tree, Naïve Bayes and Support Vector Machine. According to the results, the support vector machine showed better performance than the other algorithms. It was also found that the next day trading volume and the number of comments have a significant correlation and the results of Granger causality test showed can be used to predict stock price and also it took advantage of the aggregation of users' daily emotions. Manuscript profile
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        42 - A comparative study between the effectiveness of ARIMA and ARFIMA models in predicting the interest rate and the treasury exchange rate in Iran
        mohadeseh razaghi hashem nikomaram Alireza Heidarzadeh Hanzaei farhad ghaffari Mahdi Madanchi Zaj
        Due to the importance of predicting economic variables, different models have been created to predict the future values of variables. In fact, economic models can be tested by checking the level of forecasting accuracy. The main purpose of this study is prediction of Ir More
        Due to the importance of predicting economic variables, different models have been created to predict the future values of variables. In fact, economic models can be tested by checking the level of forecasting accuracy. The main purpose of this study is prediction of Iran interbank offered rate and Iran treasury exchange rate as interest rates indicators for facilitating interest rate risk management. Two econometric models including ARFIMA and ARIMA have been used for forecasting. Thus, the ARFIMA model considering long-term memory and the ARIMA model without considering long-term memory have been considered. The evaluation of the prediction accuracy of the two models using the monthly Iran interbank offered rates data and also the monthly Iran treasury exchange rates data shows that both the interbank offered rates data and the Islamic treasury bond rates data, ARIMA model has a better performance compared to ARFIMA model in predicting data. Manuscript profile
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        43 - Proposing a novel model based on ARIMA technique for forecasting housing price: a case study of Tehran
        Hosseyn Mombeyni Morteza Hashempoor Shahla Roshandel
        Determination and prediction of housing price in urban areas plays a significant role for governments, public and private enterprises, and financial evaluators. An accurate estimation of the housing price can be employed for future planning and decision making in many u More
        Determination and prediction of housing price in urban areas plays a significant role for governments, public and private enterprises, and financial evaluators. An accurate estimation of the housing price can be employed for future planning and decision making in many urban and regional policies. However, the growth of the housing sector has a profound impact on gross national product, resulting in a significant increase in employment. On the other hand, an increase in loan for purchasing house leads to a rise in liquidity and inflation rate. This means that the gap between the income and housing price is increased. Therefore, it is necessary to develop new models for making decisions in order to prevent the increase in the inflation rate and housing price. According to the key importance of housing price, a number of models have been developed to formulate the price behavior with regard to its effective components. In this study, a novel model based on the ARIMA method for forecasting and formulating the housing price has been developed. The results show that the model proposed has a high potential (with a determination coefficient of 99.7%) to foresee the housing prices in the city of Tehran. Manuscript profile
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        44 - Designing a Hybrid Intelligent Model for Prediction of Stock Price Golden Points
        Mohammad Moshari Hosein Didehkhani Kaveh Khalili Dameghani Ebrahim Abbasi
        The purpose of this research is to provide an intelligent model for prediction of golden points on stock price chart as a decision support system. For conduction of this research, the data of the automotive and parts manufacturing industry during 2001 through to 2016 we More
        The purpose of this research is to provide an intelligent model for prediction of golden points on stock price chart as a decision support system. For conduction of this research, the data of the automotive and parts manufacturing industry during 2001 through to 2016 were used. First, the obtained results from application of different forecasting models based on data mining were compared with each other. Next, the research variables were optimized by genetic algorithm and remodeling took place. The results indicated that the golden points could be predicted with reasonable accuracy and optimization did not enhanced accuracy in all these models, yet it significantly reduced gross error.     Manuscript profile
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        45 - Comparing the performance Of Artificial Neural Networks(ANN) and Auto Regressive Moving Average(ARIMA) Model in Modeling and Forecasting Short-term Exchange Rate Trend in Iran
        Abbas Ali Abunoori Fardad Farokhi Seyedeh Fatemeh Shojaeyan
        Exchange rate and its related fluctuation plays a significant role as one of the most important issues of each country's foreign trade sector. Many factors such as economic, politics, and psychological factors impress on exchange rates and these factors create more unce More
        Exchange rate and its related fluctuation plays a significant role as one of the most important issues of each country's foreign trade sector. Many factors such as economic, politics, and psychological factors impress on exchange rates and these factors create more uncertainty situations. Policymakers’ attempt is to reduce this uncertainty via forecasting this variable with minimal error.Artificial neural networks have high potential in modeling complex processes and forecasting dynamic nonlinear paths .Therefore, in this study has tried to use the  artificial neural network (ANN) In addition to modeling and forecasting daily exchange rates during the period of  March 2002 to March 2005, and minimizing the forecast error by this method, its results were compared with that of ARIMA based on forecasting accuracy evaluation criteria , and to examine the sensitivity of model results toward exchange rates.Estimation of the model with the same method for three data sets exchange rate including dollar,euro and pound have been performed .Results indicate that used neural network has better predictive power in comparison with arima model while  pound and Euro exchange rates’ prices are function of their yesterday prices and dollar exchange rate price is a function of its price over the past 6 days .   Manuscript profile
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        46 - Parameter setting of technical analysis indicators using multi-objective particle swarm optimization and adaptive fuzzy inference system
        Ibrahim Abbasi Hossein Akefi Shahaboddin Adibmehr
        In this paper, we propose automatic stock trading system which combines technical analysis and adaptive neural fuzzy inference system to predict the stock price trend to increase return of investment. In this trading system, at first the optimal value of technical indic More
        In this paper, we propose automatic stock trading system which combines technical analysis and adaptive neural fuzzy inference system to predict the stock price trend to increase return of investment. In this trading system, at first the optimal value of technical indicator's parameters is determined by using multi-objective particle swarm optimization and according to these parameters; technical indicators are calculated to predict stock price changes with the help of adaptive neural fuzzy inference system. We have chosen eight different stocks from Tehran stock exchange to test our trading system for two months. A computational experience is carried out in order to analyze the proposed algorithm and the obtained results are compared with usual conventional methods which have been proposed in previous researches. The computational results show our proposed method performs better than other previous methods and obtains superior results. Manuscript profile
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        47 - A study on how managers of mutual funds invest in Iran (With an approach based on technical & fundamental analysis and Modern Portfolio Theory)
        Mohammadreza Nikbakht Yasser Kargari Mahtab Davarzadeh
        This study (for the first time) examined the way investment managers of mutual funds invest, by using questionnaire method. Respondents to the questionnaire are investment managers of mutual funds in Iran. The investment managers are asked to determine the use of each o More
        This study (for the first time) examined the way investment managers of mutual funds invest, by using questionnaire method. Respondents to the questionnaire are investment managers of mutual funds in Iran. The investment managers are asked to determine the use of each of methods such as technical & fundamental analysis, Modern Portfolio Theory, bulletins, confidential information and market rumors, for market forecasting and selecting assets properly and timely. The research statistic population includes investment managers of active mutual funds in Iran. Questionnaires were sent to all of them, and 40 percent of the questionnaires were returned. The findings of this study show that investment managers always pay most of their attention to fundamental analyses and then published bulletins; they rarely use technical analysis indicators (especially in the long term) and Modern Portfolio Theory (especially in the short term). Also investment managers are reluctant to use confidential information and market rumors in any time intervals. Manuscript profile
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        48 - Forecasting Tehran’s bourse price index using return-based fuzzy time series
        Farid Radmehr Naser Shams Gharneh
        During the recent years extensive researches have been done on fuzzy time series. In many of these studies, universe of discourse and relevant intervals have been determined based on levels of price or data; in this study a new type of universe of discourse is establish More
        During the recent years extensive researches have been done on fuzzy time series. In many of these studies, universe of discourse and relevant intervals have been determined based on levels of price or data; in this study a new type of universe of discourse is established based on rate of return concept in financial markets.  Another point that has a significant effect on the performance of fuzzy time series models is the length of intervals, therefore doing research in this area became an interesting topic for time series researchers, there are some studies on this issue but their results are not good enough. So we propose a novel simulated annealing heuristic algorithm that is used to promote the accuracy of forecasting. The experimental results show that proposed model (RBFTS) is more accurate than existing models on forecasting Alabama university enrollments data. At the final step, Tehran’s bourse price index (TEPIX) is used as a case study for forecasting. The obtained results indicate a good forecasting performance on this test problem. Manuscript profile
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        49 - Day-ahead stock price forecasting using hybrid model
        Vahid Vafaei Ghaeini Alimohammad Kimiagari
        Forecasting financial markets is an important issue in finance area and research studies. Importance of forecasting on one hand and its complexity, on the other hand, researchers have done much work in this area and proposed many methods. In this research, we propose a More
        Forecasting financial markets is an important issue in finance area and research studies. Importance of forecasting on one hand and its complexity, on the other hand, researchers have done much work in this area and proposed many methods. In this research, we propose a hybrid model include wavelet transform, ARMA-EGARCH and NN for day-ahead forecasting of stock market price in different markets. At first WT is used to decompose and reconstruct time series into detailed and approximated parts. And then we used ARMA-EGARCH and NN models respectively for forecasting details and approximate series. In this model we used technical index by approximate part to the improvement of our NN model. Finally, we combine prediction of each model together. For validation, proposed model compare with ANN, ARIMA-GARCH and ARIMA-ANN models for forecasting stocks price in UA and Iran markets. Our results indicate that proposed model has better performance than others model in both markets.       Manuscript profile
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        50 - پیش بینی الگو برای واحدهای تصمیم گیرنده در تحلیل پوششی داده ها
        مرتضی شفیعی فرهاد حسین زاده لطفی هیلدا صالح
        اگرچه تحلیل پوششی داده­ها یک ابراز قدرتمند برای ارزیابی عملکرد واحدهای تحت ارزیابی می­باشد ولی این تکنیک دارای محدودیت­هایی نیز می­باشد. به عنوان نمونه یکی از محدودیت­های این روش، ارزیابی عملکرد  سیستم براساس ورودی و خروجی­های قدیم است بنابر More
        اگرچه تحلیل پوششی داده­ها یک ابراز قدرتمند برای ارزیابی عملکرد واحدهای تحت ارزیابی می­باشد ولی این تکنیک دارای محدودیت­هایی نیز می­باشد. به عنوان نمونه یکی از محدودیت­های این روش، ارزیابی عملکرد  سیستم براساس ورودی و خروجی­های قدیم است بنابراین نتایج ارزیابی به­دست آمده از مدل­های کلاسیک DEA، برای پیش بینی تغییرات کارایی واحدها در آینده و در نتیجه ارایه الگوی مناسب برای رسیدن به یک واحد کارا، کاربردی نمی­باشد . بنابراین هدف این مقاله پیشنهاد یک روش جدید به منظور پیش بینی کارایی سیستم براساس ورودی و خروجی شبیه سازی شده با استفاده از سیستم پویا و تکنیک­های شبیه سازی است. زیرا با پیش بینی کارایی واحد تحت ارزیابی، مدیران در یک سیستم می­توانند برنامه ریزی دقیق­تری برای آینده داشته باشند. برای این منظور با استفاده از یک حلقه بازخورد، ورودی­ها و خروجی­ها در واحدهای تصمیم گیرنده در آینده مورد پیش بینی قرار گرفت سپس با استفاده از مدل CCR و ورودی­ها و خروجی­های پیش­بینی، شده به پیش بینی کارایی واحد تحت ارزیابی پرداختیم. Manuscript profile
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        51 - Short-Term Load Forecasting using an Ensemble of Artificial Neural Networks: Chaharmahal Bakhtiari Case
        E. Faraji M. Mirzaeian H. Parvin A. Chamkoorii Majid Mohammadpour
        Short-term load forecasting is very important in electrical marketing. Load forecasting is dependent on climatic condition of every region and the previous structures of electrical consumption in that region; so we have accomplished this through employing climatic data More
        Short-term load forecasting is very important in electrical marketing. Load forecasting is dependent on climatic condition of every region and the previous structures of electrical consumption in that region; so we have accomplished this through employing climatic data (including temperature and pressure) and real load consumption of Chaharmahal Bakhtiari. We have evaluated our method using four machine learning algorithms: artificial neural networks (multilayer perceptron), ensemble of artificial neural networks, support vector machine and ensemble of support vector machine. Experimental results indicates that ensemble of artificial neural networks is superior to the others in the field of load consumption forecasting of Chaharmahal Bakhtiari. Manuscript profile
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        52 - Monitoring and forecasting of land use change by applying Markov chain model and land change modeler (Case study: Dehloran Bartash plains, Ilam)
        Seyed Reza Mir Alizadehfard Seyedeh Maryam Alibakhshi
        Nowadays modeling and forecasting of land use changes by application of satellite images can be a very useful tool for describing relations between natural environment and human activities to help planners to make decisions in complicated conditions. There are various m More
        Nowadays modeling and forecasting of land use changes by application of satellite images can be a very useful tool for describing relations between natural environment and human activities to help planners to make decisions in complicated conditions. There are various methods for forecasting of land uses and coverage, in which the Markov chain model is one of them. In this research, land use changes in Bartash plain in Dehloran which is located in Ilam province in the area of 135244 hectares in 3 time periods (1988, 2001 and 2013) of landSat satellite images, providing land use map in 6 classes (low density forest, medium-dense grassland, poor grassland, agricultural, alluvium sediments and non-vegetated lands) by application of  Kohonens neural network and also Markov anticipation model and Land change modeler (LCM) approach was predicted for the year 2030. The classification results showed the rate of demolition and a reduction of the area of low density forests and medium grassland land uses and increase in area of other land uses. Reduction of low density forest and the medium grassland area and increasing growth of other land uses demonstrated the overall destruction in the region and replaced with poorer land uses. At the end, by application of the Markov chain model and LCM modeling approach, land use changes were a forecasted for the year 2030. The results of changes anticipation matrix based on maps of years 2001 and 2013 showed that it is likely that in the period of 2013-2030, 45% of low density forest, 71% of medium grassland, 96% of poor grassland, 81% of agricultural lands, 93% alluvialvium sediments and 100% of non-vegetated lands remain changeless; non-vegetated lands have the most stability and low density forest have the least stability. Manuscript profile
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        53 - Integration of population forecasting in providing decision support system for municipal solid waste landfill siting (Case study: Qazvin province)
        Zahra Asadolahi Naghmeh Mobarghei Mostafa Keshtkar
        10.30495/girs.2020.675967
        Background and ObjectiveRapid urban expansion along with population growth, has significantly amplified the production of municipal solid waste (MSW) in recent years. Despite the importance of burying solid waste as one of the most efficient ways in waste management cyc More
        Background and ObjectiveRapid urban expansion along with population growth, has significantly amplified the production of municipal solid waste (MSW) in recent years. Despite the importance of burying solid waste as one of the most efficient ways in waste management cycle, its basic standards have been neglected in many parts of Iran. Recently, the Geographic Information System (GIS) has been recognized as a suitable tool in landfill site selection studies. In addition, Multi-Criteria Decision Making (MCDM) has been introduced as a well-known technique to investigate complex decision-making issues such as landfill selection, and the Analytical Hierarchy Process (AHP) is one of the well-known methods of MCDM.  In general, landfill siting based on GIS has two main screening steps including first, removing unsuitable land areas and then ranking remaining areas. Additionally, waste landfill siting mainly depends on information availability related to population characteristics. In this way, it is needed to forecast population in the future. Qazvin as a province in the central part of Iran, is facing a population growth in the recent decade. Comparing the population in 2011 and 2016, it showed an increase of about 1.17 percent of average annual growth in Qazvin's population. Therefore, with regard to the increasing population in this newly established province, it is considered critical to conduct a landfill site selection procedure. To achieve the aim, the present research intended to establish a landfill site regarding environmental factors and using integrated GIS-AHP approach which incorporated into the population forecasting in Qazvin province.Materials and Methods The present study was conducted in three main steps include; initial waste Landfill siting using Multi-Criteria Evaluation (MCE), determination of the required landfill area based on population forecasting up to 2046 and final locating of waste landfills using Single Objective Land Allocation (SOLA) in TerrSet software. In the first step, the initial Landfill siting was conducted by the integrated GIS-AHP approach during the process of identifying and selecting the criteria, weighting the criteria, standardizing the criteria and finally integrating the criteria with the Weighted Linear Combination (WLC) method. In the second step, the area required for waste disposal sites was estimated based on population growth rate, per capita waste generation (kg per day) and average groundwater depth. In order to forecast the population growth up to 2046, reports of Iran's Plan and Budget Organization was used.  In the third step, the final sitting of the municipal solid waste was determined with a SOLA in TerrSet software. The initial suitability map was entered into the model as the base input. Also, the estimated area from the second step. In this study, two scenarios were implemented.  In the first scenario, in order to select the appropriate locations, the condition of having the highest value of the map was applied, and in the second scenario, in addition to the mentioned condition, the need to have a 10 km buffer for each of the selected options was considered.Results and Discussion According to the expert's opinions and environmental standards, seven ecological and socio-economic criteria were suggested that each criterion consists of several sub-criteria. Then by implementing the AHP method on the experts’ judgment, the final weight of each criterion and sub-criterion was obtained. After preparing the GIS layers, each of the invoice layers was standardized according to the functions in the fuzzy membership tool and was classified with a range of numbers from 0 to 255. The results showed that in the study area the combination of AHP and GIS for landfill siting is significantly compatible with field observations. GIS is a very powerful tool that could provide a quick assessment of the study area to determine the appropriate location for landfill. The selection of criteria was one of the most important steps in this research. The environmental factors should be considered along with economic factors in choosing a landfill site. Therefore, the eight main criteria of distance from the road, elevation, slope, distance from residential areas, distance from surface waters, distance from protected areas, geology, hydrology and land use were used in their research. The criteria were divided into three parts; morphological, environmental and socio-economic. In this research, in addition to the mentioned criteria, various natural and human parameters such as distance from energy transmission lines, distance from industrial towns and railways, etc. were also used to double the comprehensiveness of the present study. By integrating standardized GIS layers with WLC method, the initial map indicating the distribution of suitability of different sites to waste disposal location in Qazvin province was prepared. By implementing the AHP method into each criterion and combining in GIS, the waste disposal areas in the study area were classified into four classes. According to this classification, the initial map was divided into very good, appropriate, inappropriate and very poor areas.  According to the initial suitability map, the cities of Takestan, Abik and Buin Zahra, with an area of 50.15, 14.55 and 54.48 km2, respectively, had a good condition for landfill location. The suitable places for landfill were the flat territories near the urban and had the advantage of the appropriate access path. Then, using land use allocation algorithm, the best landfill site was identified in two scenarios and three location options for each scenario. In the first scenario, the maximum map value was applied to select the location options. In the second scenario, in addition to the mentioned condition, a 10 km buffer was considered for each location option. Finally, site number one of the first and second scenarios and site number three of the second scenario were identified as priorities. Site number one was selected in the range of Buin Zahra city and near the village of Elahabad. While site number three was located 15 km away from the waste management department of Qazvin city and near the Zinabad village.Conclusion It should be noted that not only the final location of municipal solid waste landfills has not been determined in recent years, but also a comprehensive program in the field of reducing waste production and implementation of waste separation plans from the source in the studied cities has not been implemented.Formation of a future forecasting section on the organizational structure of landfill waste management systems can not only reduce environmental risks but also bring sustainability to economic and social resources. Manuscript profile
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        54 - Monitoring and predicting land use changes using landsat satellite images by Cellular Automata and Markov model (Case study: Abbasabad area, Mazandaran province)
        Amer Nikpour Hamid Amounia Elahe Nourpasandi
        10.30495/girs.2021.678602
        Background and ObjectiveToday, land use change in many countries has become an important challenge that has many effects on the environment. Accordingly, the study of land use change at different scales is one of the important issues in the proper management of natural More
        Background and ObjectiveToday, land use change in many countries has become an important challenge that has many effects on the environment. Accordingly, the study of land use change at different scales is one of the important issues in the proper management of natural resources and environmental change at various levels. Therefore, being aware of land use changes and investigating their causes and factors in several time periods, and predicting land use changes in the future can be properly planned to reduce adverse effects, which has been considered by planners and city managers. They help in land use planning. Also, converting land uses to each other and changing the use of vegetation is known as an important issue. Therefore, the purpose of this study is to monitor and predict land use changes and land cover in Abbasabad urban area in the future; Using these changes, appropriate management measures can be taken to preserve and rehabilitate lands. Materials and Methods A combination of an automated cell model and Markov chain in the Abbasabad urban area was used to predict land use change; The relevant images were taken from the TM and OLI sensors of the Landsat 8 and 5 satellites at the USGS site. Four user classes, including zone class built with code number 1, vegetation class with code number 2, water resources class with code number 3, and barren land class with code number 4, were separated for Abbasabad urban area. Obtained USGS. In order to extract land use classes, after checking several methods, object-oriented classification method and support vector machine (SVM) algorithm were used due to better efficiency. Evaluation of Babian satellite imagery classification The overall accuracy and kappa coefficient were performed for three periods of time. Each of these classified maps was evaluated by drawing an error matrix. 250 sample points were used to prepare this matrix. The type of sampling was stratified sampling. Also, to determine land use changes in 2030, classified maps were used and with the help of TerrSet software, changes made in classes and their percentages were obtained, and using the CA-MARKOV model, changes of different classes based on matrices. The possibility of transfer was predicted. Results and Discussion The results during 1997, 2006, and 2017 show that the constructed area has an increasing trend and the uses of vegetation, barren lands, and water resources have a decreasing trend and 23279 hectares of lands in the region are built area dedicated. The kappa coefficient calculated for 1997, 2006, and 2017 is 0.86, 0.89, and 0.89, respectively. Markov chain forecasting model with 85% accuracy stated that the trend of land use change for 2030 will be the same as in previous years, and this indicates that the conversion and change of land uses will proceed as before, and it is necessary to mention this point that the identical uses of vegetation to vegetation cover the largest area during the years 2006 to 2017, and this shows that in this area, vegetation is still stable and has undergone less changes. Conclusion The output of the 13-year forecast map for 2030 in this study indicates the appropriate accuracy of the CA-MARKOV model. In addition, this output shows that this method can be trusted for short-term planning. These forecast maps can be a good guide for managers and urban planners. To achieve better results, it is recommended to use a combination of automated cell model and Markov chain to monitor and predict changes nationwide. The results of this study, in addition to helping to reduce the volume of input data, but also in the processing of classified images and in predicting them for the future. Manuscript profile
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        55 - A Hybrid Method for Long-Term Demand Forecasting in the Electrical Energy Supply Chain of Basic Metal Production Industries in the Presence of Incomplete Data
        Sepehr Moalem Roya M.P. Ahari Ghazanfar Shahgholian Majid Moazzami Seyed Mohammad Kazemi
        10.30486/teeges.2023.1974408.1051
        The economic growth of any country has a lot to do with the infrastructure of the electrical energy supply chain and the ability to access it at low cost. Increasing the resilience of the electric energy supply chain in order to be able to respond to the real time deman More
        The economic growth of any country has a lot to do with the infrastructure of the electrical energy supply chain and the ability to access it at low cost. Increasing the resilience of the electric energy supply chain in order to be able to respond to the real time demand of high-consumption and strategic consumers is a challenge that will not be possible without considering long-term demand forecasting and integrated development planning of this chain. This paper presents a long-term demand forecasting approach in the electrical energy supply chain of Isfahan's Espidan iron stone industries. This approach is a combination of wavelet transform, long short-term memory (LSTM) network and finally integrating the results with data-mining technique based on machine learning. The company studied in this research is one of the main suppliers of raw materials in the supply chain of basic metal production industries and one of the ten energy-intensive industries in the electrical energy supply chain of Isfahan province. The only information available from this company is the daily time series signal of the historical electrical energy demand of this industry in a period of 40 months. The data in the studied time series is interrupted so that only 50% of the data has a value and the remaining 50% is zero. This lack of data and the impossibility of access to supplementary data and effective features for forecasting has reduced the density of data and the possibility of long-term demand forecasting faces more problems than continuous time series. The used statistical analysis showed that the annual and seasonal data do not follow the normal distribution and have high distortion and heterogeneity. The proposed method and its results have been compared with other available approaches. The results of 10 iterations of extreme learning machine methods show that the RELM technique with a high confidence level of 95% is more effective than other machine learning methods and has more accurate results. Manuscript profile
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        56 - Presenting a new model for rapid diagnosis of acute respiratory diseases using machine learning algorithms
        Mehran Nezami Avaz Naghipour Behnam Safiri Iranagh
        Corona virus, Severe Acute Respiratory virus and swine flu is a disease caused by acute respiratory syndrome. These viruses require advanced tools to identify dangerous mortality factors with high accuracy due to their immediate spread among humans. Machine learning met More
        Corona virus, Severe Acute Respiratory virus and swine flu is a disease caused by acute respiratory syndrome. These viruses require advanced tools to identify dangerous mortality factors with high accuracy due to their immediate spread among humans. Machine learning methods directly address this issue and are essential tools for understanding and guiding public health interventions. In this article, machine learning is used to investigate demographic and clinical significance. The investigated characteristics include age, gender, fever, countries and clinical details such as cough, shortness of breath, etc. Several machine learning algorithms have been implemented and applied on the collected data, the K-Nearest Neighbor algorithm works with the highest accuracy (more than 97%) to predict and select features that correctly represent the status of viruses. Manuscript profile
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        57 - Applying Optimized Mathematical Algorithms to Forecast Stock Price Average Accredited Banks in Tehran Stock Exchange and Iran Fara Bourse
        Negar Aghaeefar Mohammad Ebrahim Mohammad Pourzarandi Mohammad Ali Afshar Kazemi Mehrzad Minoie
        10.22034/amfa.2019.580802.1150
        Manuscript profile
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        58 - Information Asymmetry with Emphasis on the Role of Financial and Managerial Criteria Based on Fuzzy Logic and Artificial Neural Networks
        Mohammad Amir  Golshani Mehrdad Ghanbari Babak Jamshidi Navid Forouzan  Mohammadi Yarijani
        This paper addresses the absence of a suitable criterion for measuring information asymmetry between managers forecasting earnings and analysts forecasting earnings through statistical methods. Besides, this paper aims to provide a model of information asymmetry, emphas More
        This paper addresses the absence of a suitable criterion for measuring information asymmetry between managers forecasting earnings and analysts forecasting earnings through statistical methods. Besides, this paper aims to provide a model of information asymmetry, emphasizing the role of financial and managerial criteria. This is applied qualitative and quantitative research (mixed method). The library method is used to prepare and formulate theoretical bases. In addition, the field method is used for collecting data to measure and identify indices and modeling. Factor analysis was used to analyze the data, following identifying the dimensions and variables of financial and managerial criteria of information symmetry to eliminate extraneous factors and classify. The following five main dimensions were determined, including corporate profit forecast, corporate governance, capital market, capital return, and management characteristics of the company. Then, the modeling was done using fuzzy mathematics through triangular numbers, Mamdani implication, and center of gravity methods. The final results of the study of the company listed on the Tehran Stock Exchange show that the level of information symmetry in the range of zero to 100 equals 55.1, to predict the company's profit is 48.54; corporate governance is 56.95; the capital market is 1/59; capital return is 61.07, and managerial characteristics of the company are 67.84. Finally, we examined the factors affecting the information asymmetry obtained from fuzzy neural networks. The findings show a higher prediction accuracy of fuzzy neural network methods than other related prediction methods. Manuscript profile
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        59 - Interval Forecasting of Stock Price Changes using the Hybrid of Holt’s Exponential Smoothing and Multi-Output Support Vector Regression
        Sayyed Mohammadreza Davoodi Mahdi Rabiei
        10.22034/amfa.2020.1883402.1332
        Manuscript profile
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        60 - Investigate the Economic Consequences to the Timing of Earnings News Forecast for Accepted Corporates in Tehran Securities Exchange
        Asghar Karimi Khorami Alireza Zareie Sodani Saeed Ali Ahmadi
        10.22034/amfa.2021.1924367.1570
        Manuscript profile
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        61 - Hybrid Multilayer Perceptron Neural Network with Grey Wolf Optimization for Predicting Stock Market Index
        Meysam Doaei Seyed Ahmad Mirzaei Mohammad Rafigh
        10.22034/amfa.2021.1903474.1452
        Manuscript profile
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        62 - A Study of the Effective Factors on Error of Forecasting Technical Analysis Indicators in Iran Stock Exchange (NNARX Approach)
        Hamed Tavakolipour Faegh Ahmadi Bizhan Abedini Mohammad Hossein Ranjbar
        10.22034/amfa.2023.1972923.1823
        Manuscript profile
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        63 - Comparison of Selected Performance of Portfolio Investment Companies by Using of Grey Forecasting and Johnson’s Index in Tehran Stock Exchange Market
        Rahmatollah Mohammadi pour Zhaleh Alavimoghadam Adel Fatemi
        10.22034/amfa.2016.527813
        Manuscript profile
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        64 - Application of HS Meta-heuristic Algorithm in Designing a Mathematical Model for Forecasting P/E in the Panel Data Approach
        Mozhgan Safa Hossein Panahian
        10.22034/amfa.2018.539132
        Manuscript profile
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        65 - Forecasting Stock Trend by Data Mining Algorithm
        Sadegh Ehteshami Mohsen Hamidian Zohreh Hajiha Serveh Shokrollahi
        10.22034/amfa.2018.539138
        Manuscript profile
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        66 - Machine learning algorithms for time series in financial markets
        Mohammad Ghasemzadeha Naeimeh Mohammad-Karimi Habib Ansari-Samani
        10.22034/amfa.2020.674946
        Manuscript profile
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        67 - The Analysis of Forecasting the Monthly Trend According to Different Price Levels of Agricultural Crops (A Case Study Tomato & Potato )
        Seyed Ehsan Zohoori reza moghaddasi einollah hesami
        10.30495/sarj.2023.1982071.1124
        What is aimed in this research is to determine that are forecasts in years relatively high-price trend and low-price trend affected and smoothed according to the monthly potato and tomato prices? The analysis and forecasting of prices in harvesting seasons of two produc More
        What is aimed in this research is to determine that are forecasts in years relatively high-price trend and low-price trend affected and smoothed according to the monthly potato and tomato prices? The analysis and forecasting of prices in harvesting seasons of two products are implemented by “t” test and linear regression during 1996-2021. The results have showed significant forecasts for years with normal and high price levels meanwhile the research assumption of forecasting of the price trend has been approved more for potato in a high price level. The results of this research and similar cases can be used to forecast prices for production management of such crops, market regulation and consumers’ welfare of country in different seasons of the year. In case of extra supply with decreasing price level or lack of production with increasing price level one of approaches is to present template and compulsory cultivation in accordance with competitive advantages in provinces and different regions Manuscript profile
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        68 - پیش بینی و آنالیز حساسیت تبخیر ماهانه از مخزن سد سیاه بیشه با استفاده از شبکه‌های عصبی مصنوعی در ترکیب با الگوریتم ژنتیک
        آزاده محمدیان شوئیلی حسن فتحیان مهدی اسدی لور
        فرآیند تبخیر، یکی از مؤلفه‌های اصلی چرخه آب در طبیعت است که نقش اساسی در مطالعات کشاورزی، هیدرولوژی و هواشناسی، بهره برداری از مخازن، طراحی سیستم‌های آبیاری و زهکشی، زمان بندی آبیاری و مدیریت منابع آب ایفا می‌کند. روش‌های زیادی از جمله روش‌های بیلان آب، تبخیر از تشت و ر More
        فرآیند تبخیر، یکی از مؤلفه‌های اصلی چرخه آب در طبیعت است که نقش اساسی در مطالعات کشاورزی، هیدرولوژی و هواشناسی، بهره برداری از مخازن، طراحی سیستم‌های آبیاری و زهکشی، زمان بندی آبیاری و مدیریت منابع آب ایفا می‌کند. روش‌های زیادی از جمله روش‌های بیلان آب، تبخیر از تشت و روش‌های تجربی برای تخمین تبخیر از سطح آزاد، ارائه شده است که هر کدام از این روش‌ها،  با محدودیت و خطای اندازه گیری توأم می‌باشد. امروزه تکنیک جدید استفاده از شبکه‌های عصبی مصنوعی که مبتنی بر هوش مصنوعی می‌باشد کاربرد گسترده ای در زمینه‌های مختلف علمی به ویژه مهندسی آب پیدا کرده است. در این تحقیق با استفاده از مدل شبکه عصبی مصنوعی پرسپترون چند لایه(MLP)، شبکه تابع پایه شعاعی (RBF) و شبکه پیش رونده(FF)،میزان تبخیر ماهانه از مخزن سد سیاه بیشه تا 3 ماه آیندهپیش بینی شد. برای تعیین متغیرهای ورودی مؤثر در مدل‌های شبکه عصبی مصنوعی و تعداد نرون‌ها در لایه میانی هر یک از مدل‌ها، از قابلیت بهینه سازی الگوریتم ژنتیک استفاده شد. نتایج نشان می‌دهد که ضریب همبستگی بین مقادیر اندازه گیری شده و محاسبه شده با مدل‌های RBF ، MLPو  FFدر برآورد و پیش بینی تبخیر ماهانه از مخزن سد سیاه بیشه به ترتیب برابر با 92/0، 90/0 و 88/0 می‌باشد. بنابراین مدل RBF از دقت بیشتری نسبت به دو مدل MLP وFFدر برآورد و پیش بینی میزان تبخیر ماهانه از مخزن سد،  برخوردار می‌باشد. نتایج حاصل از آنالیز حساسیت نشان می‌دهد که تبخیر ماهانه از مخزن سد تا 3 ماه آینده به ترتیب نسبت به زمان وقوع تبخیر بر حسب ماه، فشار هوا در سطح زمین در 1 ، 3 و2 ماه قبل، سرعت باد در سطح 1000 میلی بار در 3 و 2 ماه قبل و دمای هوا در سطح 300 میلی بار در زمان حال بیشترین حساسیت را دارد. Manuscript profile
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        69 - Determining the optimal forecasting combination of the four-level supply chain to minimize the bullwhip effect
        Maryam Daneshmand-Mehr Marzban Najafi Ramin Sadeghian
        Bullwhip effect that occurs in the chain, leads to inefficiencies such as excess inventory and overdue orders during the chain. These problems can be reduced by appropriate predictions. Forecasting must be done in all levels of a supply chain. This paper addresses the p More
        Bullwhip effect that occurs in the chain, leads to inefficiencies such as excess inventory and overdue orders during the chain. These problems can be reduced by appropriate predictions. Forecasting must be done in all levels of a supply chain. This paper addresses the problem of optimal combination of forecasting to reduce the bullwhip effect in the four-level supply chain. For this purpose, a four-level supply chain is considered. One of the methods such as moving average, exponential smoothing, linear regression and multilayer perceptron artificial neural network can be considered for predicting in each level. First, the desired supply chain is simulated for this means. The different combinations of aforementioned forecasting methods are calculated. Then a combination of forecasting methods according to minimized bullwhip effects is selected. Finally, the results are analyzed by variance analysis model. Two combinations have the lowest bullwhip effects. Moving average, neural networks, exponential smoothing and linear regression for levels: retailer, wholesaler, manufacturer and supplier respectively as an answer and the other is: moving averages, exponential smoothing, neural network and linear regression in the same mentioned levels and other combinations have less utility. Manuscript profile
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        70 - Predicting Land Changes in River Margin and Urban Areas by Remote Sensing and GIS
        ehsan izadi Ali Akbar Jamali
        Today, the rapid growth of the world's urban population, especially in developing countries, hascreated many problems in various fields. Among these, land-use change is of great importance.Modeling and predicting future land-use changes has become increasingly important More
        Today, the rapid growth of the world's urban population, especially in developing countries, hascreated many problems in various fields. Among these, land-use change is of great importance.Modeling and predicting future land-use changes has become increasingly important for urban andenvironmental management and other relevant authorities and researchers. The main purpose of thisstudy is to apply cellular automata (CA) Markov models based on spatial information system tosimulate and predict land-use change. Landsat satellite imagery was prepared during the three periodsof late June 1986, 2001, and 2016. Then land use maps of the study area were obtained by classifyingthe maps. The model derived from the CA Markov was implemented to predict and process and toanalyze land-use changes by 2031. Forecast results showed that from 2016 to 2031, green space, urbanresidential land use increased and the agricultural and open land use declined. This study will generallyshow the decline in open land and agriculture and the expansion of residential and urban areas in 2031,which was caused by the loss of agricultural land and vegetation. The region's economy, based onagricultural and livestock production will face the current productivity situation in 2031. Manuscript profile
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        71 - Neural Networks in Electric Load Forecasting:A Comprehensive Survey
        Vahid Mansouri Mohammad Esmaeil akbari
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        72 - Forecasting the exchange rate using futures studies methods and examining the effect of currency fluctuations on the performance of companies: A case study of Iran Tobacco Company
        Alireza Fathinia Ali Badizadeh
        Exchange rates always have a high priority and attractiveness in society, especially among companies. Different methods are used to predict the exchange rate, among which structural methods as methods of fundamental analysis, a little precision in advance. They have an More
        Exchange rates always have a high priority and attractiveness in society, especially among companies. Different methods are used to predict the exchange rate, among which structural methods as methods of fundamental analysis, a little precision in advance. They have an accurate forecast of the exchange rate, but they are very useful as a long-term perspective and illuminate the movement of the exchange rate. Technical compensation can be used to compensate for the shortcomings of these methods. Using futures research techniques, in addition to covering the study gap of futures research techniques in forecasting exchange rates, errors due to quantitative methods have been minimized so that companies are prepared for the occurrence of various situations. In the final part of the article, the effect of currency fluctuations on the performance indicators of the tobacco company during the last three years is examined. The results of the study indicate that currency fluctuations have the first and greatest impact on the implementation of development projects and also sudden economic shocks are not immediately reflected in the performance of companies and due to the presence of shock absorbers such as inventory in Warehousing, borrowing purchases and long-term debt creation, over time, gradually affect and weaken the company's performance. Manuscript profile
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        73 - Forecasting Daily Volatility and Value at Risk with High Frequency Data
        Amir Mohammad Zadeh Sahar Masoud Zadegan
        One of the key aspects in the financial markets and its development is fluctuation. Fluctuation plays a key role in option pricing, portfolio management and the market sentiment. In general, financial institutions are faced with four various kinds of risk, which are cre More
        One of the key aspects in the financial markets and its development is fluctuation. Fluctuation plays a key role in option pricing, portfolio management and the market sentiment. In general, financial institutions are faced with four various kinds of risk, which are credit risk, liquidity risk, operational risk, and market risk. The most appropriate method to measure the market risk is by using the VaR (value at risk). Value at Risk is statistical technique used to measure and quantify the level of financial risk within the investment portfolio over a specific time frame. It is always expressed by the monetary amount that is at risk as well as the probability of loss. This research is to predict the VaR for a one-day period in six different industries in which three companies are monitored in each industry. The time periods of the study are 30-minute intervals between 91/11/1 to 92/4/1,  in which the GARCH model is used for predicting the variance. The research then checks to see whether the data fits the normal or t-distributions models. Thus, six models are used for six different industries. All six chosen models are deemed proper to predict the coefficients, how fit the coefficients are, and Watson statistic camera. The estimation of the variance and the Var for all models is done at a %95 confidence interval. The research concludes that the companies involved in the basic metals group are more prone to risk and have higher VaR in comparison to other industries. Manuscript profile
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        74 - An Assessment Method for Project Cash Flow under Interval-Valued Fuzzy Environment
        Vahid Mohagheghi SEYED meysam mousavi Behnam Vahdani
        10.22094/joie.2017.278
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        75 - Three Approaches to Time Series Forecasting of Petroleum Demand in OECD Countries
        Majid Khedmati Babak Ghalebsaz-Jeddi
        10.22094/joie.2018.538229
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        76 - Optimization of Inventory Controlling System Using Integrated Seasonal Forecasting and Integer Programming
        Hagazi Heniey Kidane Gebrehiwot Tsegay Desta Leake Gebrehiwot
        10.22094/joie.2021.1895036.1732
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        77 - The Influence of Globalization Processes on Forecasting the Activities of Market Entities
        Nazariy Popadynets Olga Vyshnevska Inna Irtyshcheva Iryna Kramarenko Maryna Ponomarova
        10.22094/joie.2021.1945341.1909
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        78 - Forecasting Seasonal and Trend-Driven Data: A Comparative Analysis of Classical Techniques
        Zahira MARZAK Rajaa BENABBOU Salma MOUATASSIM Jamal BENHRA
        10.22094/joie.2023.1984123.2057
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        79 - Optimizing the Prediction Model of Stock Price in Pharmaceutical Companies Using Multiple Objective Particle Swarm Optimization Algorithm (MOPSO)
        Ali Khazaei Babak Haji Karimi Mohammad Mahdi Mozaffari
        10.22094/joie.2020.677889
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        80 - Forecasting the Cost of Water Using a Neural Network Method in the Municipality of Isfahan
        Amir Mohammadzadeh Nasrin Mahdipour Arash Mohammadzadeh
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        81 - Analysis of the Earth Dams Function against the Effects of Long-Term Deposition in Reservoirs (Polrood Earth Dam-Guilan Province)
        Mahdi keshavarz Nasser Shamskia
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        82 - Trip Forecasting Process Modeling in Urban Transportation Planning Based on Hybrid Fuzzy Inference Approach
        Javad Jassbi Payam Makvandi
        Urban Transportation Planning (UTP) has been one of the most important decisions in urban planning and development procedures in recent years. Meanwhile, accurate trip forecasting between two given regions of the city could be considered as the key success factor of urb More
        Urban Transportation Planning (UTP) has been one of the most important decisions in urban planning and development procedures in recent years. Meanwhile, accurate trip forecasting between two given regions of the city could be considered as the key success factor of urban transportation planning. Due to the importance of the problem, different models have been developed in the field. The overall problem of trip forecasting and transportation planning could be complicated because of its nature that results from the complicated nature of human behavior. Due to the complexity of the problem, it is always hard to develop forecasting models with acceptable forecasting errors and also low computational expenses particularly in developing countries in which historical data are not fully available.  In this paper, a three phase fuzzy model is proposed to forecast trips flow between two given regions of a metropolitan based on mapping demographical and social variables to total number of trips flow. The overall model is to explore the subjective pattern of transportation experts and transfer the subjective model to a mathematical framework.   Manuscript profile
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        83 - Improving the Efficiency of Forecasting Productivity, Using a Taguchi Experiment Design Approach (Case Study: Food Industries in Iran)
        Seyed mahmon Zanjirchi Mehdi Hatamimanesh Hamedreza Kadkhodazadeh Seyedali Mohammadbanifatmi
        Productivity forecasting is a key factor in strategy planning in an organization. Artificial neural networks method is one of the productivity estimating methods whose users must have enough experience and skill because of its adjustable parameters. Trial and Error is m More
        Productivity forecasting is a key factor in strategy planning in an organization. Artificial neural networks method is one of the productivity estimating methods whose users must have enough experience and skill because of its adjustable parameters. Trial and Error is mostly used to find the proper levels of these parameters. This article presents a seven step pattern for selecting proper adjustable parameters for neural network, using Taguchi experiment design method to improve the efficiency of productivity forecasting. As a result, the optimum parameters levels that lead to the most desirable forecasting in neural network are as follows: the number of hidden layers: 2 layers, the number of neurons in each hidden layer: 7 neurons, learning rate: 0.9 and the number of neural network inputs:  productivity indicators with more than 0.85 degree of correlation. Among the above mentioned factors, the number of hidden layers with 71.18% of contribution rate in experiment results is the most important factor in neural network design to forecast the productivity of Iranian food industry. Finally, the overall results of the study showed that using this pattern provides the possibility of choosing competitive strategies besides decreasing forecasting time and cost. Moreover, this pattern helps decision makers with the extent of the consideration that must be put into each adjustable parameter by determining the contribution rate of each parameter in the experiment results. Manuscript profile
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        84 - Structural analysis of Urban flourishing with a futuristic approach (Case Study: Ahwaz Metropolis)
        mohammadali firoozi Fereshteh shanbehpoor
        Urban prosperity is a concept related to balanced development and harmonized in the environment is committed to justice and embodies human activity as a kind of social construction. Nowadays a different view of the future has caused man not to seek the future, but to bu More
        Urban prosperity is a concept related to balanced development and harmonized in the environment is committed to justice and embodies human activity as a kind of social construction. Nowadays a different view of the future has caused man not to seek the future, but to build his desired future by using various tools. On the other hand, the challenge of new urban life, environmental and social problems lead to future research approach in urban planning and using various devices to build a favorable future. The present study uses the technique of cross-impact analysis, which is one of the common methods in futurism by using MicMac software, which it has analyzed the components of urban prosperity in Ahvaz metropolis. In the following using the Delphi method which 30 components in five areas such as (productivity, infrastructure, quality of life, equality and social participation, and environmental sustainability) extracted as indicators of urban prosperity. The results indicate that there are five categories in the scattering pages like (influencing factors, bilateral factors, regulatory factors, influential factors and independent factors) are identifiable. Finally among the 30 mentioned factors after examining the effectiveness of these factors on each other and on the future situation of the metropolis of Ahvaz by direct and indirect methods, 7 key factors in (literacy rate, slum families, air pollution, unemployment rate, cultural centers, life expectancy at birth and poverty rates) have been selected to play the crucial role in the future situation of urban prosperity in Ahvaz. The results show that none of the components of urban prosperity in Ahvaz can be defined by experts as a target factor. This issue indicates the multifaceted issue of prosperity of Ahvaz metropolis. Manuscript profile
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        85 - Assessment of NARX Neural Network in Prediction of Daily Precipitation in Kerman Province
        Kamal Omidvar Maasomeh Nabavizadeh Meysam Samarehghasem
        Precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. Precipitation forecasting has important role in management More
        Precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. Precipitation forecasting has important role in management and warning of this problem. Due to the interaction of various meteorological parameters in the calculation of rain, leads it to a very irregular and chaotic process. The purpose of this study, assessment of forecasting precipitation, using data from meteorological stations of the using common statistical period (2012-1989) in Kerman, Baft, Miandeh Jiroft. In this way, to the training of the artificial neural networks with structure Perceptron, Nonlinear Autoregressive External. Effective Factors in the rain, as input for Artificial Neural Networks and precipitation was considered as the output of the Network. Statistic indicators MSE, R were used for performance evaluation of the models. The analysis of output results from, Nonlinear Autoregressive External Neural Networks shown that these models have better accuracy and a high ability to forecast precipitation than Perceptron Neural Networks. The results showed the more exact method concerned to the (NARX) model. The 42 models with all parameters with Levenberg Marquat rule and sigmoid function had the best topology of the model in three stations. Overall, evaluation of NARX results showed that the errors of ANN were negligible. The NARX showed high sensitivity to relative humidity. Manuscript profile
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        86 - Subject: Forecasting Occurrence of Radiation Frost withmeteorologicalMinimum Data, Case Study :Shahroud(Semnan Province)
        Gholamreza Janbazghobadi
        Frost is one of the major climatic Hazards that creates  irreparable damages  in various communities every year. Amongst different kinds of frosts, radiation frost is very important; because of frequency occurrence and iscontrollable well with active protectio More
        Frost is one of the major climatic Hazards that creates  irreparable damages  in various communities every year. Amongst different kinds of frosts, radiation frost is very important; because of frequency occurrence and iscontrollable well with active protection methods in agricultural sector. Along with radiation frost monitoring,Precise forecasting of the minimum temperature and hourly estimation of its variations (trend) during the nights with frost event for starting and ending time determination of the active protection methods is satisfied.Therefore, using an experimental forecasting model, which can calibrate to meet local conditions and have a simple application, it seems essential. Thereafter, this study aimed at predicting the minimum temperature using a Polynomial model. In this paper, using hourly synoptic data of shahroud station for January, February during 1984-2010, dry temperature after sunset embedded for developing the prediction model of minimum temperature. Then, according to the predicted minimum temperature, the temperature trend during the night hours with radiation frost event was incident prediction., Pearson correlation coefficient value between observed and predicted minimum temperatures based on developed model was In the significant confidence level of high. The amount of root mean square error (RMSE) for the developed model is 0.2 °C for January. Manuscript profile
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        87 - A New Hybrid Model Using Deep Learning to Forecast Gold Price
        Mohammad Reza Shahraki
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        88 - Forecasting Future Trends of the Stock Market Using the Probit Regression Approach with Emphasis on Value at Risk
        Seyed Ali Mousavi Loleti Emran Mohammadi Saeed Shavvalpour
        Forecasting has always been recognized as an important issue in financial markets and is considered a unique factor in estimating future unknown values. The aim of this research is to identify and forecast the conditions of the Tehran Stock Exchange(TSE) and the factors More
        Forecasting has always been recognized as an important issue in financial markets and is considered a unique factor in estimating future unknown values. The aim of this research is to identify and forecast the conditions of the Tehran Stock Exchange(TSE) and the factors affecting them, focusing on the correlation between market prosperity and value at risk. To achieve this, in the first step of this study, the time series of the value at risk index on the capital market TSE was estimated using daily data and the first-order GARCH method from spring 2010 to June 2023. Then, the factors influencing prosperity in TSE were evaluated based on seasonal data from spring 2010 to June 2023 using the probit regression approach. In addition, value at risk index was calculated seasonally and the relationship between the probability of market prosperity and the value at risk index was examined using correlation coefficients.The research results show that the probability of market prosperity in the Iranian capital market has a significant negative relationship with the bank interest rate, liquidity growth and the occurrence of sanctions. There is also a significant positive relationship with the inflation rate and the growth of the exchange rate. Furthermore, the correlation analysis shows that market prosperity is directly related to equity value at risk. Assuming stable conditions, the research suggests that the probability of a prosperity market in the next three seasons is significantly higher than the occurrence of a recession. Manuscript profile
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        89 - A Feature Extraction Based Long-Term Electricity load forecasting Framework to Reduce the Outliers Data Effects
        Mohammad Davoud Saeidi Majid Moazzami
        Electrical load forecasting is the prediction of future demands based on various data and factors containing different consumptions on weekdays, electricity prices and weather conditions that are different for societies and places. Generally, medium-term electrical load More
        Electrical load forecasting is the prediction of future demands based on various data and factors containing different consumptions on weekdays, electricity prices and weather conditions that are different for societies and places. Generally, medium-term electrical load forecasting is often used for the operation of thermal and hydropower plants, optimal time planning for maintenance of power plants and the power grids. However, long-term electrical load forecasting is used to manage on-time future demands and generation, transmission and distribution expansion planning. In this paper, a hybrid long-term load forecasting approach using wavelet transform and an outlier robust extreme learning machine is proposed. Hourly load and temperature data were extracted from the GEFCOM 2014 database and divided into two classes of training and test. The one-level wavelet transform is used to decompose data to extract properties and reduce the dimensions of the data matrix. Decomposed low-frequency component (approximations) and high-frequency component values (details) from wavelet analysis are entered into the model for training and forecasting. For comparison accuracy of the proposed method, wavelet transform is applied to the data for the other three extreme learning machines. Also data without wavelet transform entered into four other forecasting models and the load forecasting results are compared with the proposed method. The results of the above mentioned evaluation show that electrical load forecasting by using wavelet transform and outlier robust extreme learning machine improves forecasting accuracy and the MAPE reduces to 3.0966. The overall calculated error by the proposed method was the best result obtained between the three several models of extreme learning machines and without preprocessing model. The MAPE is 0.4208 less than the ELM, 0.944 less than the RELM, and 0.1353 less than the WRELM model, respectively. Manuscript profile
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        90 - Electricity load forecasting using hybrid models based on Multi-Layer Perceptrons Neural Network and Seasonal Auto-Regressive Integrated Moving Average models
        Fateme Chahkoutahi Mehdi Khashei
        Nowadays, saving time and economy of each country requires proper planning, decision making, and rational forecasts in different areas. One of the most well-known areas that has received a lot of attention is electricity forecasting. The features of the electricity whic More
        Nowadays, saving time and economy of each country requires proper planning, decision making, and rational forecasts in different areas. One of the most well-known areas that has received a lot of attention is electricity forecasting. The features of the electricity which makes it distinguished from other commodities are the impossibility of storing it and the existence of seasonality and nonlinear and ambiguity pattern in electricity data set. These features of the electricity makes it more difficult to forecast using traditional methods. Therefore, in this paper, a parallel optimal hybrid model using seasonal linear and nonlinear methods is proposed to forecast the electricity load forecasting. The main idea of this model is the use of the advantages of individual models in the modeling of complex systems in a structure, simultaneously. Experimental results indicate that in this method due to the use of a direct weighting method, the computational cost of modeling it is significantly lower than other parallel hybrid methods. Manuscript profile
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        91 - Long-Term Demand Forecasting in Electrical Energy Supply Chain of Espidan Ironstone Industry using Deep Learning and Extreme Learning Machine
        Sepehr Moalem Roya M. Ahari Ghazanfar Shahgholian Majid Moazzami Seyed Mohammad Kazemi
        Espidan ironstone industries is one of the most consumed power industries in the electricity supply chain of Isfahan province as the second industrial hub of the country and one of the main suppliers of raw materials in the supply chain of the country's steel industry. More
        Espidan ironstone industries is one of the most consumed power industries in the electricity supply chain of Isfahan province as the second industrial hub of the country and one of the main suppliers of raw materials in the supply chain of the country's steel industry. Planning in a large-scale electricity supply chain, in a space full of uncertainty, is begin with electricity demand forecasting.In this paper, a hybrid long-term demand forecasting method in the electricity supply chain of Isfahan's ironstone industries using a combined data mining method including wavelet transform,deep learning and intensive learning machine is proposed. The used data in this study is according to the recorded information from the electrical energy demand signal of Espidan ironstone industries in a period of 40 months in the form of 24-hours. The data in a part of the study period due to the lack of production of this industry in some hours are interrupted. So that only 40% of the data had a value and the remaining, 60% were zero. This subject led to information deficiencies and increases the forecasting error up to 40% in the first step of the proposed algorithm. By completing the first step of the proposed model with intense learning machine (ELM) the forecasting error is reduced and it was possible to create an improved forecasting model for supervised training. Finally, simulation results are compared with other available approaches such as support vector machine and decision tree. The results show the improvement and reduction of error and a significant increase in the accuracy of the proposed method in long-term demand forecasting in the electricity supply chain of Espidan ironstone industries. Manuscript profile
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        92 - Short-Term Load Forecasting of Distribution Power System for Weekdays Using Old Data
        Bahador Fani Soleyman Fehresti Sani Ehsan Adib
        Estimation of daily load in distribution companies which is performed to present the results to the DMS, is necessary. Daily load forecasting of power systems has traditionally been considered. Because load patterns are influenced by several factors such as climate, eco More
        Estimation of daily load in distribution companies which is performed to present the results to the DMS, is necessary. Daily load forecasting of power systems has traditionally been considered. Because load patterns are influenced by several factors such as climate, economy and society, it is difficult to predict the load exactly. That's why in recent years the use of intelligent algorithms to predict it, is growing. In this project, the short-term load forecasting is performed in a hybrid approach. Due to the different behavior in different days, various methods have been used to predict the load. With studying different methods of load prediction, finally, finally exponential smoothing algorithm was used to predict the exact load in the weekdays. Manuscript profile
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        93 - Novel Hybrid Fuzzy-Evolutionary Algorithms for Optimization of a Fuzzy Expert System Applied to Dust Phenomenon Forecasting Problem
        Somayeh Ghanbari Rahil Hosseini Mahdi Mazinani
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        94 - Modeling Ghotour-Chai River’s Rainfall-Runoff process by Genetic Programming
        Mina Ruhnavaz Abdolreza Hatamlou
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        95 - An Efficient Artificial Intelligence Based Technique in Diseases Staging and Forecasting
        Negar Ahmadi Alfredo Milani
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        96 - Forecasting Milled Rice Production in Ghana Using Box-Jenkins Approach
        Nasiru Suleman Solomon Sarpong
        The increasing demand for rice in Ghana has been a major concern to the government and other stakeholders. Recent concerns by the coalition for African Rice Development (CARD) to double rice production within ten years in Sub-Saharan countries have triggered the to impl More
        The increasing demand for rice in Ghana has been a major concern to the government and other stakeholders. Recent concerns by the coalition for African Rice Development (CARD) to double rice production within ten years in Sub-Saharan countries have triggered the to implement strategies to boost rice production in the government. To fulfill this requirement, there is a need to monitor and forecast trends of rice production in the country. This study employs the Box-Jenkins approach to model milled rice production using time series data from 1960 to 2010. The analysis revealed that ARIMA (2, 1, 0) was the best model for forecasting milled rice production. Although, a ten years forecast with the model shows an increasing trend in production, the forecast value at 2015 (283.16 thousand metric tons) was not good enough to compare with the current production of Nigeria (2700 thousand metric tons), the leading producer of rice of rice in West Africa. Manuscript profile
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        97 - Town trip forecasting based on data mining techniques
        Mohammad Fili Majid Khedmati
        10.30495/jiei.2020.678774
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        98 - A novel grey–fuzzy–Markov and pattern recognition model for industrial accident forecasting
        Inyeneobong Ekoi Edem Sunday Ayoola Oke Kazeem Adekunle Adebiyi
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        99 - An iterative method for forecasting most probable point of stochastic demand
        J. Behnamian S. M. T. Fatemi Ghomi B. Karimi M. Fadaei Moludi
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        100 - An investigation of model selection criteria for technical analysis of moving average
        Milad Jasemi Ali M Kimiagari
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        101 - Modeling and forecasting US presidential election using learning algorithms
        Mohammad Zolghadr Seyed Armin Akhavan Niaki S. T. A. Niaki
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        102 - Forecasting time and place of earthquakes using a Semi-Markov model (with case study in Tehran province)
        Ramin Sadeghian
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        103 - Development of an evolutionary fuzzy expert system for estimating future behavior of stock price
        Azam Goodarzi Amirhossein Amiri Shervin Asadzadeh Farhad Mehmanpazir Shahrokh Asadi
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        104 - A new probability density function in earthquake occurrences
        S Sadeghian G.R Jalali-Naini
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        105 - A hybrid computational intelligence model for foreign exchange rate forecasting
        M Khashei F Mokhatab Rafiei M Bijari S.R Hejazi
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        106 - optimal model sales in gold ounce and s&p 500 markets on the basis of optimal stopping
        amir mahmoudian maryam khalili araghi hamidreza vakilifard
        10.30495/ecomag.2024.2001797.1094
        Extended Abstract Throughout history, predicting price in financial markets has always been of high interest to financial activists and analysts. Recently, various methods have been proposed and adopted to predict the dynamism of financial markets using time series o More
        Extended Abstract Throughout history, predicting price in financial markets has always been of high interest to financial activists and analysts. Recently, various methods have been proposed and adopted to predict the dynamism of financial markets using time series of records of prices. However, high-precision predication of financial prices is still a deemed long-term challenge that constantly call for state-of-the-art approaches. Purpose Thus, the purpose of the current study was to examine the efficacy of optimal stopping, also known as early stopping and use its connection with branching processes to predict the optimal buying and selling prices in several financial markets. gold ounce market and S&P 500 index have been predicted in short term and long-term frameworks on the basis of fixed horizon. And for each. frame work, different time frames have been selected. Closing price data from 1995 to 2022 have been used for every time frame. Methodology Advanced methods for optimal stopping include approximating the value function and then using that approximation in a policy. Although such policies can work very well, they are generally not guaranteed to be interpretable (Siokan and Mišić 2020). On the other hand, some researchers have proposed that the optimal stop models are too complicated to solve well and the strategies of buying at a low price and selling at a high price are not very practical in this theory (Liu and Mo 2022). According to the issues raised, the researcher intends to use the optimal stop theory and its connection with branch processes to implement and examine this theory in a number of prominent international financial markets. In this research, we are going to use the optimal stop statistical theory to predict the time of buying and selling in these markets in an optimal way. Finding The optimal stopping algorithm seeks to determine the maximum value from a set of random variables that are exhibited in the order they are generated. Each variable should either be selected when exhibited or be skipped in favor of the next variable, and if all the variables till the nth variable are skipped, this variable is selected automatically. It should be borne in mind that the theory of optimal stopping first examines the previous data and finds out whether there is a divergence, according to which it determines which market cannot be predicted based on this theory. In general, these random variables are considered as independent and co-distributed. Yet, due to the complexity of this theory, even in this case, solving problems directly proves to be very difficult, and hence the correlation between this theory and branch processes are employed to simplify solution. The steps of this process are as follows: Step 1 The analyst finds the planning horizon (20 - horizon in the present paper) Step 2 Determine the statistical distribution of values using statistical tests, including the goodness of fit, chi-square, Chebyshev's inequality, and Q-Qplot (Moud et al. 1973) Step 3 Transforming it to a normal distribution using Box-Cox Transformations (Cox-Box 1964), and converting to the standard normal distribution (minus mean value divided by standard deviation) (Moud et al. 1973) Step 4 Using inverse distribution function (using probability integral transform theorem) (Moud et al. 1973) and transforming it to considered distribution in branching process and determining convergency or divergence of data (Ross 1983; Shishebor et al. 2004) Step 5 Predicting the best point for optimal buy or sell at a determined horizon (Assaf et al. 2000) Step 6 Reversing all transforms and predicting real values (Assaf et al. 2000) Conclusion The results indicate that by optimal stopping for short term framework, S&P 500 index indicates %67 success and gold ounce shows %53 success in the prediction of prices. In long term framework, S&P 500 index's success equals to %68 and gold ounce equals to %85 in prediction of prices. The obtained results show that the optimal stop theory has performed better in predicting the gold price in the long-term time frame and the S&P 500 in the short-term time frame. The S&P 500 market and the gold market have obtained the most predictability based on the optimal stop theory. This can be confirmed by market traders because the S&P 500 and gold market are interesting markets from a technical and trading point of view. The number of transactions and high liquidity and the difference in spread and commission in these two markets compared to other markets can be indicative of this. Also, the high volatility in the two mentioned markets due to the uncertainty regarding the continuation of prices and key economic indicators presents countless opportunities to traders. According to the obtained results, the optimal stopping can be used as a trading and analytical indicator. Also, the characteristic of optimal stopping is that based on historical data, it shows the prediction as the optimal point (buying and selling price) in the future. Due to the fact that examining the financial markets both from the study and analytical point of view and from the trading point of view by using a variety of forecasting patterns and indicators requires understanding the possibilities of market behavior, choosing a time frame and having a strategy. Manuscript profile
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        107 - Long-term Streamflow Forecasting by Adaptive Neuro-Fuzzy Inference System Using K-fold Cross-validation: (Case Study: Taleghan Basin, Iran)
        Reza Esmaeelzadeh Alireza Borhani Dariane
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        108 - Using Smooth Transition Regression (STR) to predict Business Cycles
        Harmony Shahmoradi Hamid Abrishami Oranus Parivar
        Forecasting business cycles is very important in macroeconomic and it is an important part in process of economic decision-making and policy. In recent years, non-linear models have been considered more for forecasting economic variables and application of these models More
        Forecasting business cycles is very important in macroeconomic and it is an important part in process of economic decision-making and policy. In recent years, non-linear models have been considered more for forecasting economic variables and application of these models has been made a significant improvement in modeling of the behavior of variables in the area of macroeconomic and particularly financial economics. This article provides a convenient and powerful model for forecasting business cycles by using smooth transition regression (STR). The results show that very little error that indicates model performance is acceptable. Manuscript profile
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        109 - The Relationship Between Cash Flow Forecasting and Cost of Owners’ Equity
        Ali Baghani narges ghorbani
          Cash flow forecasting is an important factor in many economic decisions because cash flows play an important role in almost all decision making by groups such as securities analysts, creditors and managers (Sloan, 1996). To discover this fact, the relationship b More
          Cash flow forecasting is an important factor in many economic decisions because cash flows play an important role in almost all decision making by groups such as securities analysts, creditors and managers (Sloan, 1996). To discover this fact, the relationship between the cash flow forecasting and the cost of owners’ equity was investigated. In this study, using the data analysis, the relationship between cash flow forecasting and cost of owners’ equity was examined. The statistical sample of research consisted of 167 companies listed in Tehran Stock Exchange during the 6 years and a multivariate regression method were used to test the hypotheses for gathering information. Test of the hypothesis and finally analysis of the data were done using Excel software as well as statistical software Spss23, Eviews9, Minitab19.The following models have been used to test the hypothesis according to Sang Hwan Yaongs paper (2015). According to the number of variables studied, multiple regression was used. The results of the research show that there is no significant relationship between cash flow forecasts and the cost of owners’ equity. Manuscript profile
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        110 - A Wavelet Transform-Based Hybrid Short Term Load Forecasting Method for Managing the Costs of EV Charging Stations and Parking Lots
        Yashar Khanchoupani Mojtaba Beiraghi Reza Ghanizadeh
        10.30495/spre.2023.707656
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        111 - مقایسه قدرت پیش بینی منحنی فیلیپس کینزین جدید هایبریدی و مدل ARIMA از تورم
        زهرا افشاری مرضیه بیات
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        112 - ارزیابی مدل‏های خطی و غیرخطی در پیش‏بینی شاخص قیمت سهام در بورس اوراق بهادار تهران
        علی اکبر خسروی نژاد مرجان شعبانی صدر پیشه
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        113 - An Analisis of Leading indicators for the Irans Economy
        Esfandiar. Jahangard Alireza. Farhadikia
        Effectively predicting cyclical movements in the economy is a major challenge. In this paper we evaluate the role of a set of variables as leading indicators for Iranian economy. Our evaluation is based on using the annual and seasonal variables trend. For this purpose, More
        Effectively predicting cyclical movements in the economy is a major challenge. In this paper we evaluate the role of a set of variables as leading indicators for Iranian economy. Our evaluation is based on using the annual and seasonal variables trend. For this purpose, we introduce a model based on NBER approach to estimate leading and coincident indicators covering the period 1367:Q1-1386:Q4.We apply ARMA models for the forecasting of leading and coincident indicators for the period 1387:Q1-1388Q4.The forecasting results show that the will be decline of leading and coincident macroeconomic indicators in 1387 in Iran economy Manuscript profile
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        114 - پیش‌بینی قیمت بنزین فوب خلیج‌فارس با استفاده از مدل‌های ARIMA و ARFIMA
        حمید آماده فرشید عفتی باران امین امینی
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        115 - Fuzzy intelligent forecasting approaches and tools in the field of digital currencies: A systematic review
        Davood ZareKhaneghah Ali Mohammadi Mohammad Imani Barandagh Amir Najafi
        10.30495/fed.2024.709350
        Abstract Digital currency, is one of the most important factors in the success of organizations that will be present in the arena of global competition. In the present review, the most important theories of digital currency forecasting based on fuzzy hybrid models and More
        Abstract Digital currency, is one of the most important factors in the success of organizations that will be present in the arena of global competition. In the present review, the most important theories of digital currency forecasting based on fuzzy hybrid models and artificial neural networks have been systematically investigated. These models mainly focus on supervised methods for measuring hybrid models. Also, basic concepts about the history of hybrid models from the first proposed models to current developed models, their combinations and architectural capabilities, data processing and measurement methods of these intelligent models are presented so that evolution This category of intelligent systems is analyzed. Finally, the features of prominent (leading) models and their applications in digital currency forecasting are presented. The results show that fuzzy neural network models and their derivatives are efficient in predicting digital currency with very high accuracy and with good justification capability that is used in a wide range of economic and scientific fields. Manuscript profile
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        116 - Investigating the relationship between Integrated Report Quality and earnings forecasting bias and Share Price Informativeness
        Muhammad Vahdani Javad Muhammadi Mehr
        10.30495/faar.2024.709444
        Abstract Integrated financial reporting provides investors with a comprehensive understanding and insight into the company and reduces the costs of obtaining information, processing, facilitating and combining relevant and effective information, and ultimately raising More
        Abstract Integrated financial reporting provides investors with a comprehensive understanding and insight into the company and reduces the costs of obtaining information, processing, facilitating and combining relevant and effective information, and ultimately raising stock price awareness, improving information quality and facilitating more efficient capital allocation. The purpose of this study is to investigate the relationship between integrated financial reporting with earnings forecasting bias and stock price awareness. The statistical population of this research is all companies listed on the Tehran Stock Exchange and 167 companies in the period 1391 to 1399 have been selected by systematic elimination method. Also, to test the research hypotheses, a multivariate regression model based on composite data was used. The results of the first hypothesis indicate that there is a direct and significant relationship between integrated financial reporting and behavioral financial bias in stock price forecasting. The results of testing the second hypothesis showed that there is a direct and significant relationship between integrated financial reporting and accuracy of profit forecasting. Integrated financial reporting with disclosure of financial and non-financial information from the financial reporting process and corporate activity makes investors and analysts more accurate forecasts. The results of testing the third hypothesis showed that there is a direct and significant relationship between integrated financial reporting and stock price awareness. He said that by combining financial and non-financial information, the process of creating value for the company in the form of financial reporting makes the information content of stock prices informative and investors in stocks make optimal decisions according to rational investment theory. Manuscript profile
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        117 - Relationship between Management Profit Forecasting Error and Adjustment of Profit Forecast with Corporate Accruals
        Alireza Rahimi Aref Foroughi Majid Azadi
        10.30495/faar.2022.693674
        AbstractNet profit and its adjustments are among the most valuable information used by investors. This study seeks to answer the question whether company conditions (ambiguity or inability to understand economic information) affect the relationship between forecasting e More
        AbstractNet profit and its adjustments are among the most valuable information used by investors. This study seeks to answer the question whether company conditions (ambiguity or inability to understand economic information) affect the relationship between forecasting error and adjustment of profit forecasting by management or not? For this purpose, first, the relationship between accruals (abnormal) and error and adjustment of profit forecast, test and then the effect of ambiguity and inability to understand economic information on the above relationship is examined. The research sample includes 91 companies listed on the Tehran Stock Exchange. Findings indicate that there is a positive and significant relationship between working capital accruals and abnormal working capital accruals with management forecast error, but there is a negative relationship between working capital accruals and abnormal working capital accruals wThere is significance. Also, conditions of ambiguity did not affect this relationship, but the inability to understand economic information strengthens this relationship.ith negative forecast and adjustment.  Manuscript profile
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        118 - Short Term Load Forecasting by Using ESN Neural Network Hamedan Province Case Study
        Milad Sasani
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        119 - A Hybrid GA-Modified Harvey Model for Short-term Forecasting of Day-ahead Electricity Price and Electricity Load
        Mehdi Abroon Alireza Jahangiri Ahmad Ghaderi Shamim
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        120 - A Machine Learning Approach to Detect Energy Fraud in Smart Distribution Network
        Mahdi Emadaleslami Mahmoud-Reza Haghifam
        10.30495/ijsee.2021.683248
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        121 - Hourly Wind Speed Prediction using ARMA Model and Artificial Neural Networks
        Farzaneh Tatari Majid Mazouchi
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        122 - The Explanation of the Relationship between Downside Risk and Upside Risk combination in predicting Market Return Volatility
        hossein rad kaftroudi mohammadhasan gholizadeh mahdi fadaei
        The volatility of financial returns plays an important role in many empirical applications, such as portfolio allocation, risk management and derivative pricing. The purpose of this research is to explain the relationship between undesirable risk and desirable risk in p More
        The volatility of financial returns plays an important role in many empirical applications, such as portfolio allocation, risk management and derivative pricing. The purpose of this research is to explain the relationship between undesirable risk and desirable risk in predicting market return volatility. The research is descriptive in nature and applied in purpose. The statistical population of the study is the companies listed in Tehran Stock Exchange and the target sample of the companies listed in the cement industry from which the required research data can be extracted. The research period is from 1392 to 1397. This research has a theoretical model and the self-regression model was used to test the hypotheses. In the cement industry, according to the t-statistic and its coefficient of determination, it is clear that the predictor of market yield fluctuations correlates with undesirable and desirable risk. Also, the adjusted coefficient of determination is 51%, which indicates this effect. Manuscript profile
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        123 - Application of econometric modeler for predicting stock prices in the capital market
        Alireza Sadat Najafi Soheila Sardar
        Investing in the capital market requires deciding on issues such as selection, timing, price and share buybacks with market research. One of the ways to do this is to use econometric modelers. In the studies performed to compare methods or to present hybrid models, most More
        Investing in the capital market requires deciding on issues such as selection, timing, price and share buybacks with market research. One of the ways to do this is to use econometric modelers. In the studies performed to compare methods or to present hybrid models, most econometric models have been studied without comparing and predicting the error of prediction error of other algorithms. In this research, the most efficient algorithm for solving this defect is implemented and compared with the proposed methods on selected shares and based on the proposed parameters.On the other hand, often the order of the regression and the mean of the moving average sentence are considered for the finite number of studies, which is based on Bayesian criteria for determining the p and q degrees to obtain the optimal response. This paper compares the methods of self-regressive moving average, cumulative self-regressive moving average, self-regulated seasonal moving average, self-regressive moving average with explanatory variable, cumulative mean self-regression with explanatory variable, self-regression model with variance. Generalized conditional, exponential self-regression model with generalized conditional heterogeneity variance and regression model with moving average self-regression errors for selected symbols of Tehran Stock Exchange. Manuscript profile
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        124 - Factor Variability Test in Stock Return Forecasting Using Dynamic Model Averaging (DMA)
        hosein maghsoud hamedreza vakilifard Taghi Torabi
        In this study, using dynamic averaging models and monthly data in the period 2001:4 until 2018:3, Tehran Stock Exchange returns be investigated. In this regard, macroeconomics variables and parallel markets indices have been used to forecast the stock returns. Initially More
        In this study, using dynamic averaging models and monthly data in the period 2001:4 until 2018:3, Tehran Stock Exchange returns be investigated. In this regard, macroeconomics variables and parallel markets indices have been used to forecast the stock returns. Initially, estimating various models such as Recursive models, time-varying parameter models (TVP), dynamic model selection (DMS) and dynamic model averaging (DMA) in Matlab software, It was observed that DMS model with α = β = 0.95 had higher forecast accuracy (based on MAFE, MSFE and Log (PL) metrics). Gold price (48-period), exchange rate (36-period) and inflation rate (30-period) had the highest effect on stock returns, respectively, and global oil prices and GDP had the lowest effect by 28 and 2, respectively. Finally, the results indicate that utilizing dynamic models by considering time variations in parameters and the variation of the model increases the efficiency of forecasting stock returns. Keywords: Forecasting, Stock Returns, time-varying Parameter (TVP), Dynamic Model Averaging (DMA). Manuscript profile
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        125 - The Impact of Using Dimensionality Trading Strategies on Forecasting the Daily Stock Returns of the Panel Data Method.
        Ehteram Rahdarpoor heshmatolah asgari
        Earnings forecasting systems provide timely decisions by providing timely information. Earnings forecasting by management is widely used in assessing profitability, profit-related risk, stock price judgments, and valuation models (Manfred & Inky, 2014). Our purpose More
        Earnings forecasting systems provide timely decisions by providing timely information. Earnings forecasting by management is widely used in assessing profitability, profit-related risk, stock price judgments, and valuation models (Manfred & Inky, 2014). Our purpose in this study is to investigate and investigate the impact of dimensionality trading strategies on predicting daily stock market returns by the fuzzy logic approach of firms. This study is a library-analytic-causal study based on panel data analysis (panel data). In this study, the financial information of 19 companies listed in Tehran Stock Exchange during the period 2011-2018 was reviewed. The results showed that using stock trading strategy and stock price reduction strategy have significant effect on prediction of daily stock market returns, but trading volume reduction strategy has no significant effect on market forecasting. I hope to accept my article. I suggest the editor remove this restriction on the number of words used in the abstract for the English text. Manuscript profile
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        126 - Design of Decision Support System to Forecast Demand for Dynamic Network Design Based on Uncertainty and its Impact on Economic Justification
        Mohammad Mokhtari Aboutorab Alirezaei Hassan Javanshir Mahmoud Modiri
        Minimize supply chain costs as one of the essential issues in support activities such as financial planning systems,How to manage supply chain A set of ways to integrate Effective suppliers, manufacturers, warehouses and stores used to minimize total supply chain costs More
        Minimize supply chain costs as one of the essential issues in support activities such as financial planning systems,How to manage supply chain A set of ways to integrate Effective suppliers, manufacturers, warehouses and stores used to minimize total supply chain costs and meet customer service needs with a high level of service. In this study, the design of a robust cement supply chain dynamic network model was designed to reduce supply chain management costs after a crisis. Principal and efficient design of cement grid infrastructures, given the strong demand fluctuations at different times of the year, can significantly reduce financial costs on the one hand and reduce the potential for high-speed, high-cost corruption by correct prediction. Other leads.From the following tool The nose has been analyzed using artificial neural networks. The purpose of this study is to use artificial intelligence methodologies such as Grid Clustering, Subtractive Partitioning, FCM to explore fundamental and technical patterns and relationships in historical data. Used. To this end, a genetically-based inference fuzzy multilayer fuzzy neural network is introduced to prevent technical and economic unpredictability. The basic model of this paper presented by the researcher is a robust and multi-periodic planning for multi-product state under uncertainty. Manuscript profile
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        127 - Designing a Model for Forecasting the Stock Exchange Total Index Returns (Emphasizing on Combined Deep Learning Network Models and GARCH Family Models)
        Mehdi Zolfaghari Bahram Sahabi Mohamad javad Bakhtyaran
        Given the development of machine learning models in predicting financial data in recent years, this study introduces a combination of Deep Learning Network and selected GARCH family models to predict short-term daily returns of the Tehran Stock Exchange Index. The most More
        Given the development of machine learning models in predicting financial data in recent years, this study introduces a combination of Deep Learning Network and selected GARCH family models to predict short-term daily returns of the Tehran Stock Exchange Index. The most important feature of the deep learning network is that it can adapt and adjust itself to the volatility of market variables without being limited to specific models. In this study, short-term and long-term memory based neural network (RNN-LSTM) models are used for deep learning network models and GARCH and EGARCH models are used in its structure. Also, the two independent variables of oil price and dollar rate in the structure of the hybrid model help to predict the financial data more accurately. Comparison of the results of hybrid model prediction error with individual models shows that the RNN-LSTM-EGARCH hybrid model has higher prediction accuracy than competing models. competing models. Manuscript profile
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        128 - Comparison of different machine learning models in stock market index forecasting
        maryam sohrabi Seyed Mozaffar mirbargkar Ebrahim Chirani SINA KHERADYAR
        Predicting time series of financial markets is a challenging issue in the field of specialized studies of time series and has attracted the attention of many researchers. Due to the presence of big data, this issue has led to the growth of developments in the field of m More
        Predicting time series of financial markets is a challenging issue in the field of specialized studies of time series and has attracted the attention of many researchers. Due to the presence of big data, this issue has led to the growth of developments in the field of machine learning models. Due to the importance of this issue, in this study, by using the comparison of different machine learning models such as random forest approaches, support vector machine, artificial neural network and deep learning-based recurrent neural networks to investigate the ability of different machine learning models in prediction. The total index of Tehran Stock Exchange during the period 2013 to 2020 has been discussed. The prediction results of 1, 3 and 6 day courses for the out-of-sample period show that the machine learning method based on the long short-term memory (LSTM) network, a recurrent neural networks, has a better result compared to other models. Manuscript profile
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        129 - Comparison of multiple linear regression and machine learning algorithms inPredicting cash holdings
        samira seif mostafa yousofi tezerjan
        In recent years, in the financial literature, more attention has been paid to the level of cash holding of companies. So; Forecasting is important to determine the optimal level of cash holding. In this research, using linear and non-linear methods and 13 influential in More
        In recent years, in the financial literature, more attention has been paid to the level of cash holding of companies. So; Forecasting is important to determine the optimal level of cash holding. In this research, using linear and non-linear methods and 13 influential input variables, the amount of cash in 103 companies admitted to the Iran Stock Exchange during the years 2013 to 2021 has been predicted. The methods used include multiple linear regression (MLR), k nearest neighbor (KNN), support vector machine (SVM) and multi-layer neural networks (MLNN) for prediction. The results show that the traditional method of multiple linear regression has not been successful in predicting cash, but machine learning algorithms have been superior with an accuracy of 0.99. The variables of profit per share, the ratio of current assets to current liabilities and the ratio of short-term debt to total assets have had a greater impact in all algorithms. Therefore, managers can use advanced machine learning algorithms to predict the future cash flow of companies. Manuscript profile
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        130 - Applying hybrid algorithm of fuzzy time series for stock price forecasting and comparing them with calculating stock price achieved by golden ratio technique for Tehran Stock Exchange companies
        Negar Aghaeefar Mohammad Ebrahim Mohammad Pourzranadi
        The especial importance of capital market in countries is undeniable in economic development via effective capital conduct and optimum resources allocation. Investment in capital market requires decision making in new stock exchanges, and accessing information in the ca More
        The especial importance of capital market in countries is undeniable in economic development via effective capital conduct and optimum resources allocation. Investment in capital market requires decision making in new stock exchanges, and accessing information in the case of future status of capital market. Forecasting stock market price has always been a challenging task, since it is affected by many economic and non-economic factors and variables; therefore, selecting the best and the most efficient forecasting model is tough and essential. For this forecasting, we need a computing model with systematic method that can be estimated in this research. The attribution of this test considering one of the stock exchange industries is forecasting prices and contrasting them with calculated price achieved by Golden ratio algorithm. Banking industry is selected and all of listed in bourse and farabourse banks are reviewd that the results of one of them is  presented in this article.  Time-series and Fuzzy logic models are used for rationalization. Fuzzy time-series models have been utilized to make reasonably accurate prediction in the area of stock price movements.The mentioned  combined method are run on the average of weekly prices of Tehran Stock Exchange. In this research stock trade for investors with calculated relations are displayed. Manuscript profile
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        131 - Stock price prediction based on LM-BP neural network and over-point estimation by counting time intervals: Evidence from the Stock Exchange
        Mohammadreza Vatanparast masoud asadi Shaban Mohammadi abbas babaei
        In this study, to determine the stock price forecasting method, a LM-BP neural network was presented based on time series with respect to open price, highest price, lowest price, package price and volume of transactions. In the present study 315 days of stock prices wer More
        In this study, to determine the stock price forecasting method, a LM-BP neural network was presented based on time series with respect to open price, highest price, lowest price, package price and volume of transactions. In the present study 315 days of stock prices were chosen to create 10 samples and the test set includes stock prices from day 316 to day 320 and used the LM-BP neural network. In this research, the determination of the critical point of excess, asymmetry and counting of intervals were investigated. The curve MRE2-MRE1 was plotted and the precision related to the best prediction of the BP neural network was estimated based on several independent replicas. The post-test was performed using a Kupiec Test and a Christopherson test. The results showed that stock price prediction based on the LM-BP neural network and over-point estimation by counting the intervals resulted in better results than the existing methods. Manuscript profile
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        132 - Proposition of a model For Forecasting Value at Risk in One Step Ahead
        ehsan Mohammadian Amiri S. Babak Ebrahimi maryam Nezhad Afrasiabi
        Risk forecasting for future periods plays an important role in making the right decisions of managers and financial activists to invest in companies and institutions. On the other hand wrong decisions of commercial managers can have undesirable consequences for their or More
        Risk forecasting for future periods plays an important role in making the right decisions of managers and financial activists to invest in companies and institutions. On the other hand wrong decisions of commercial managers can have undesirable consequences for their organizations. Therefore the most important issues for investors is forecasting risk in future periods. The importance of this issue was caused us to forecast Value at Risk (VaR) in one step ahead by using the exponential smoothing family for two normal and t-student distributions with confidence levels of 95%, 97.5% and also 99% in this research. Previously the classic method is commonly used to forecast future periods of VaR, but in this research the family of exponential smoothing models is used, which process data by considering trend and doing so online monitoring. In order to validate the model, the proposed model has been compared with the classic method by using backtesting. The results confirms the more accurate forecasting of proposed method in normal distribution with confidence levels of 97.5%, and 99% and also in t-student distribution with confidence levels of 97.5%, 99%. Manuscript profile
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        133 - Choosing an optimal Model for Explaining & Forecasting the Volatility of Iranian Gold Price Returns: a Comparison of GARCH, IGARCH & FIGARCH Models
        Mahdi Shahrazi
        This paper compares three models of the GARCH family to investigate the volatility dynamics of gold Price returns. Nowadays, GARCH-type models have been extensively used in modeling the volatility process of various asset price returns. Gold plays a critical role as a h More
        This paper compares three models of the GARCH family to investigate the volatility dynamics of gold Price returns. Nowadays, GARCH-type models have been extensively used in modeling the volatility process of various asset price returns. Gold plays a critical role as a hedge against adverse market conditions. An accurate understanding about the gold volatility is important for the financial assets pricing, risk management, portfolio selection hedging strategies and value-at-risk policies. In this study, we use Iranian gold returns data from March 25, 2003 to December 25, 2015 and employ the GARCH(1,1), IGARCH(1,1) and FIGARCH(1,d,1) specifications. The research findings show that the FIGARCH is the best model to capture dependence in the conditional variance of the gold returns. Moreover, we examine the long memory behavior in the volatility of gold returns. According to the estimation results, the long memory parameter is positive and statistically significant. Consequently, long memory is an important characteristic of the gold volatility returns and should be taken into consideration in investment decisions. Also, the out-of-sample evaluation criteria (MAE, RMSE and TIC) select the FIGARCH(1,d,1) as the best forecasting model of gold volatility. Manuscript profile
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        134 - A Comparison between Fama and French five-factor model and artificial neural networks in predicting the stock price
        reza tehrani Milad Heyrani Samira Mansuri
        One of the most important issues of financial markets is the prediction of price and stock returns. In this paper, we try to find the best model and stock price prediction approach based on the mean square error (MSE), root-mean-square error (RMSE), R-squared, standard More
        One of the most important issues of financial markets is the prediction of price and stock returns. In this paper, we try to find the best model and stock price prediction approach based on the mean square error (MSE), root-mean-square error (RMSE), R-squared, standard deviation (SD), Mean absolute error and the mean absolute percent error (MAPE) for the Fama and French five-factor model. For this purpose, after the formation of a portfolio based on the Fama and French model during the period from 2009 to 2017, stock price is estimated by econometric model, neural network and Fuzzy Neural Networks, so the accuracy of each approach was compared. The results of the prediction the efficiency of the generated portfolios show that the prediction accuracy of the radial base function network (RBF) is very high compared to other ARMA models and other neural networks. Manuscript profile
      • Open Access Article

        135 - A simulation Model to Predict and Improve the Performance of the Working Team and Achieve better Human-Resource Management Strategies (Case Study: Department of Industrial Management, Faculty of Management and Economics, Sciences and Research Branch, Islamic Azad University)
        fatemeh eskandar reza radfar abas toloi
        Given the importance of the role of human resources in organizations, strategic planning for achieving the optimum number of human resources in an organization is vital. The purpose of this research is to present a model for simulating and predicting team performance in More
        Given the importance of the role of human resources in organizations, strategic planning for achieving the optimum number of human resources in an organization is vital. The purpose of this research is to present a model for simulating and predicting team performance in industrial management teams in order to identify human-resource performance and improve human-resource management strategies. The factors that influence team performance are identified and extracted from previous studies, and then the timing of each task is examined in three scenarios, i.e. optimistic, probabilistic and Web-based, by identifying the processes that influence team performance. Model inputs are the number of students, the number of faculty, and the number of experts. Based on related studies, team performance outputs are three categories: The number of books, articles and theses; the desirability of team members; and the number of tasks completed, rejected, needed to revise or waiting in queue. The simulation was performed using Any Logic software. The results show that the desirability of the group manager and the training expert have the highest values in most scenarios and the number of tasks in the execution queue has a significant value in all scenarios. In some cases it is essential that new policies be adopted to improve HRM strategies in the industrial management department. Manuscript profile

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