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Open Access Article
1 - Forecasting of Banks Liquidity resources
Dr.Ahmad Yazdanpanah Zahra AbbasiLiquidity 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 MoreLiquidity 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
2 - Forecast the Gold Coin Future Contracts prices by ARIMA models in Iran Mercantile Exchange (IME)
سعید علی احمدیThis paper is investigated the Forecasting the Gold Coin Future Contracts prices in Iran Mercantile Exchange (IME). In this paper survey the ability the Forecasting the Gold Coin Future Contracts by Box- Jenkins Methodology. The Box- Jenkins Methodology is included the MoreThis paper is investigated the Forecasting the Gold Coin Future Contracts prices in Iran Mercantile Exchange (IME). In this paper survey the ability the Forecasting the Gold Coin Future Contracts by Box- Jenkins Methodology. The Box- Jenkins Methodology is included the Identification, Estimation, Diagnostic Checking and Forecasting. This result indicates that the ARIMA model with the two lags of Autoregressive and with the two lags of Moving Average is appropriated to predict the Gold Coin Future Contracts prices and have the ability the Forecasting the Gold Coin Future Contracts. Manuscript profile -
Open Access Article
3 - Application of hybrid ARIMA and support vector regression model for improvement of time series forecasting
Laleh Parviz Bahareh SaeedabdiAccurate investigation related to the structure of time series plays an important role in increasing the accuracy of ARIMA forecasting. The aim of this research is to investigate the effect of modeling decomposition of linear and non linear parts of time series on ARIMA MoreAccurate investigation related to the structure of time series plays an important role in increasing the accuracy of ARIMA forecasting. The aim of this research is to investigate the effect of modeling decomposition of linear and non linear parts of time series on ARIMA model results. The decomposition of wheat and maize yield time series (in Kermanshah and Esfahan provinces) in the linear part was related to ARIMA and in the non linear part was conducted with support vector regression (hybrid model). The kind of configuration of non linear part of hybrid model is more important for example in the maize time series of Kermanshah, the values of RMSE for configuration with residual was 1.52 and for time series configuration was 15.03. The decreasing of RMSE, MAE and UII for wheat time series of Esfahan with hybrid model was 45.94%, 52.29% and 46%, respectively which is indicative of hybrid model improvement. The value of GMER in all four time series was greater than one which indicates the overestimation of hybrid model. Comparison the average of each criteria with two models and crops in each province indicated the effect of climate on modeling process because the average of criteria in Esfahan province decreased rather to Kermanshah (RMSE decreasing= 24.72%, UII decreasing=12.24%). Therefore, decomposition of time series to linear and non linear parts of time series can increase the accuracy of ARIMA model results. Manuscript profile -
Open Access Article
4 - Assessment of heavy metals concentrations and pollution in sediments of Almejogh Ophiolite Region (North-East of Iran)
Mahjoob Haghparast habiballah torshizian Rahim DabiriBackground and Objective: Heavy metals can be present at low concentrations in the soil and contaminate it. Since the study area is ophiolite in terms of lithology, it can increase the concentration of heavy metals in soil and water resources. In this study, the concent MoreBackground and Objective: Heavy metals can be present at low concentrations in the soil and contaminate it. Since the study area is ophiolite in terms of lithology, it can increase the concentration of heavy metals in soil and water resources. In this study, the concentration of heavy metals has been investigated and the pollution in sediments of Almejogh region (Fariman ophiolite) is evaluation. Method: To study the concentration of heavy metals and sediment pollution in the study area, 9 samples of sediment were taken from depth of 30 to 20 cm. The samples were transferred the environmental laboratories of Islamic Azad University of Mashhad and the amounts of pH and EC in soil were measured. 10 grams of soil (material passing through a 200-mesh sieve) was transferred to the ACME Laboratories of Canada in order to be analyzed for determining the amount of heavy metals by the induction plasma-mass spectrometry (ICP-OES) method. Discussion and Conclusion: Study of the correlation of heavy metals by Pearson coefficient, cluster analysis and principal components analysis showed that there are two different origins for geochemical distribution of heavy metals in sediment of the region. Ophiolite set of distribution of Cobalt, Nickel, Chromium and Pyroclastic and volcanic set as well as distribution of Iron elements, Molybdenum, Vanadium, Copper, Arsenic, Lead, Potassium and Cadmium were controlled. The pH of the soil in the study area fell within the pretty alkaline limit based on the classification of American soil science society. Evaluation of enrichment factor showed that Nickel has a very high enrichment and Arsenic and Chromium were in a high enrichment region. The above enrichment is indicative of anthropogenic origin. The pollution coefficient also showed that Nickel has the highest pollution in the region. The index of accumulation (of earth) also indicates t contamination of the sediment with Nickel in the region. Manuscript profile -
Open Access Article
5 - 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 ZajDue 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 MoreDue 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 -
Open Access Article
6 - Proposing a novel model based on ARIMA technique for forecasting housing price: a case study of Tehran
Hosseyn Mombeyni Morteza Hashempoor Shahla RoshandelDetermination 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 MoreDetermination 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 -
Open Access Article
7 - ARIMA and ARFIMA Prediction of Persian Gulf Gas-Oil F.O.B
H. Amadeh A. Amini F. EffatiGas-oil is one of the most important energy carriers and the changes in its prices could have significant effects in economic decisions. The price of this carrier should not be more than 90 percent of F.O.B price of Persian Gulf, legislated in subsidizes regulation law MoreGas-oil is one of the most important energy carriers and the changes in its prices could have significant effects in economic decisions. The price of this carrier should not be more than 90 percent of F.O.B price of Persian Gulf, legislated in subsidizes regulation law in Iran. Time series models have been used to forecast various phenomena in many fields. In this paper we fit time series models to forecast the weekly gas-oil prices using ARIMA and ARFIMA models and make predictions of each category. Data used in this paperstarted with the first week of the year 2009 until the first week of 2012 for fitting the model and the second week of 2012 until 13th week of 2012 for predicting the values, are extracted from the OPEC website. Our results indicate that the ARFIMA(0.0.-19,1) model appear to be the better model than ARIMA(1,1,0)and the error criterions RMSE, MSE and MAPE for the forecasted amounts is given after the predictions, respectively Manuscript profile -
Open Access Article
8 - Improving Accuracy of Tourist Demand Estimation of Asian Countries
Arshin Bakhtiari Yuhanis Abdul Aziz Azmawani Abdul Rahman Rosmah MohamedDue to the importance of accurate tourism demand estimation, the evaluation of estimating approaches is still ongoing. To address this challenge, the current study aimed to present a novel estimation statistical approach for modifying ARIMA to compare with two most prom MoreDue to the importance of accurate tourism demand estimation, the evaluation of estimating approaches is still ongoing. To address this challenge, the current study aimed to present a novel estimation statistical approach for modifying ARIMA to compare with two most prominent soft computing approaches, ANN and SVM. ARIMAadj is the modified ARIMA seasonal adjustment that declares a potential replacement to conventional ARIMA. Current study investigated the accuracy of seasonal adjustment on conventional ARIMA and compared its accuracy with ANN and SVM in estimating tourist demand of Asian countries to South Korea. The results show that the modified ARIMA outperform the soft computing approaches for tourism demand estimation accuracy of five out of six source Asian countries. Therefore, it could be concluded although there is no optimal approach to estimate tourist arrivals with certainty, the findings of this study show that the seasonal adjustment in ARIMA would be a worthwhile model to estimate tourism demand of Asian countries. Manuscript profile -
Open Access Article
9 - Role of Market Demand and Added Value in Optimizing the Iron Products
Abdollah Hadi-Vencheh -
Open Access Article
10 - Neurological Functions of CEO Investment based on Varimax Analysis and Rotated Matrix in Q Typology
Hasan Valiyan Davood Hassanpour Mehdi Safari Gerayli -
Open Access Article
11 - Determining the interest rate on deposits in the Iranian banking system: cooperative or competitive game between the central bank and followers?
Mehdi Memarpour Ashkan Hafezalkotob Mohammad Khalilzadeh Abbas Saghaei Roya Soltani -
Open Access Article
12 - الگوسازیARIMA برای بارش سالانه مشهد
آتوسا خجسته حسین عساکره -
Open Access Article
13 - ارزیابی مدلهای سری زمانی SARIMAدر برآورد جریان ماهانه در ایستگاه هیدرومتری ایدنک
عباس احمدپور حسین فتحیان جبرائیل قربانیاندبی جریان آبراههها ازجمله مهمترین داده هیدرولوژیکی هستند و به عنوان اطلاعات پایه در بسیاری از فعالیتهای مرتبط با منابع آب در نقاط مختلف جهان استفاده میشوند. یکی از ابزارهای مهم در مدلسازی فرآیندهای هیدرولوژیکی استفاده از مدلهای سری زمانی است. جریان آبراهه برآوردشده Moreدبی جریان آبراههها ازجمله مهمترین داده هیدرولوژیکی هستند و به عنوان اطلاعات پایه در بسیاری از فعالیتهای مرتبط با منابع آب در نقاط مختلف جهان استفاده میشوند. یکی از ابزارهای مهم در مدلسازی فرآیندهای هیدرولوژیکی استفاده از مدلهای سری زمانی است. جریان آبراهه برآوردشده با استفاده از مدلهای سری زمانی در مطالعات مختلفی نظیر خشکسالی، سیلاب، طراحی سیستم های مخازن و مدیریت منابع آب قابل استفاده میباشد. این امر بخصوص در مناطق خشک اهمیت بیشتری دارد. در این مقاله به ارزیابی دقت مدلهای سری زمانی SARIMA در برآورد جریان ماهانه در ایستگاه هیدرومتری ایدنک پرداخته میشود. برای این منظور از دادههای دبی جریان ماهانه این ایستگاه به مدت 30 سال، طی سالهای (1390-1361) استفاده شده است. برای صحتسنجی مدلهای سری زمانی SARIMA برازش یافته، از مقادیر آماره آزمون پورت مانتو، و باقیماندهای توابع خودهمبستگی و خودهمبستگی جزیی استفاده شد و برای انتخاب بهترین مدل SARIMA، از معیار اکائیکه اصلاحشده (AIK) و معیار بیزین شوارتز ((SBC بهره گرفتهشد. نتایج این تحقیق نشان میدهد که از بین مدلهای مناسب برازش یافته بر دبی جریان ماهانه در ایستگاه هیدرومتری ایدنک،مدلهای SARIMA(1,0,1)*(2,0,2)12،SARIMA(2,0,2)*(2,0,2)12 و SARIMA(1,0,2)*(2,0,2)12 به ترتیب در اولویت اول، دوم و سوم از لحاظ دقت در برآورد دبی جریان برخوردار می باشند. Manuscript profile -
Open Access Article
14 - The Statistical Examination of Ionic Ratio and Saturation Indexes to Investigate the Origion of Underground Water Resource Salts from Delfan
Tayebeh Karkhaneh ramin sarikhani Artims GhasemiThis research aims to examine underground water of delfan city in terms of geochemical characteristicsTo this end,the main elements of underground water were analyzed.Based on which all parameters were lower than allowed limit.As the saturation index can be an important MoreThis research aims to examine underground water of delfan city in terms of geochemical characteristicsTo this end,the main elements of underground water were analyzed.Based on which all parameters were lower than allowed limit.As the saturation index can be an important factor to understand solvation –setllement of mineral available in underground water ,the saturation index was calculated using computerized code phreeqc.The saturation index of the studied minerals in all water specimens was negative and all considered minerals can be solving.Also ,based on ionic exchange diagrams,sodium and cholor have two different origins and Calcite,Dolomit and Gypsum solvations have occurred from which Calcite and Dolomit solvations were higher.According to the HCA,samples are in two main clusters which Anions-Kations concentration in one cluster samples were higher .According to the clusters stiff diagram the region water type is Bicarbonate –Casic .To find main factor of underground water chemistry ,rotational Varimax method has been used which is the most common PCA,because it gives more interpretable elements.By this method,the limestones and Dolomit,s solvation and rock-water interaction are the most important factors of the region,s underground water chemistry Manuscript profile -
Open Access Article
15 - Forecasting Seasonal and Trend-Driven Data: A Comparative Analysis of Classical Techniques
Zahira MARZAK Rajaa BENABBOU Salma MOUATASSIM Jamal BENHRA -
Open Access Article
16 - Analysis and prediction of water level fluctuations in Urmia Lake using ARIMA model
khadijeh javan Farhad NasiriThis study has been done to evaluate the fluctuations of water level in Urmia Lake and to provide a best model for prediction the water level fluctuations. Monthly water level data for the period (1345 - 1392) was used and homogeneity was assessed by Run Test. Then the MoreThis study has been done to evaluate the fluctuations of water level in Urmia Lake and to provide a best model for prediction the water level fluctuations. Monthly water level data for the period (1345 - 1392) was used and homogeneity was assessed by Run Test. Then the stability of mean and variance of the data was tested in order to put down the non-stability by creation a rank in series. Trend of the monthly series was eliminated by making a difference and the time series of water level was evaluated by using Box- Jenkins model and the best model was fitted. Accuracy of the model was verified based on AIC, BIC and chart analysis of autocorrelation and partial autocorrelation functions and ARIMA = (0, 1, 4) (1, 1, 1)12 was selected as a suitable model. The selected model was fitted then the model was tested by Analysis of residuals and confirmed its authenticity. Finally, the monthly behavior of the series was predicted for 9 years later by using this model. Manuscript profile -
Open Access Article
17 - Time Series Models to Predict the Monthly and Annual Consumption of Natural Gas in Iran
Arash Farrokhi Reza Hassanzadeh -
Open Access Article
18 - Comparing the Performance of ARIMA and MS-AR Models to Forecast Business Cycles in Iran
Mehdi Fazel Akbar Tavakoli Mostafa RajabiIt is clear that business cycles are inevitable in economy. On the other hand, the economists are always looking for how to form business cycles and so under the effect of economic policies, since the economic situation is depended to these policies. Therefore, the acce MoreIt is clear that business cycles are inevitable in economy. On the other hand, the economists are always looking for how to form business cycles and so under the effect of economic policies, since the economic situation is depended to these policies. Therefore, the access to more precise business cycles forecasting methods would direct and manage the economic situation and policies powerfully. Hence, the main objective of this study is to construct a new model based on Markov-Switching Autoregressive (MS-AR) model to forecast the business cycles in Iran. In addition, the model constructed is compared to ARIMA to represent its power. GDP data seasonally covers the period 1989: I – 2009: IV collected from Central Bank of Iran. MS-AR and ARIMA models are applied to forecast the behavior of business cycles. By using MAPE, RMSE and Theil criteria (TIC), the results indicate that MS-AR model will work better than ARIMA to forecast GDP business cycles. Manuscript profile -
Open Access Article
19 - Stock Price Forecasting through Using ANN and ARIMA Techniques: A Case Study of Pars Petroleum Company
Seyed Nezame aldin Makian Fateme sadat MousaviStock exchange market is one of the important ways to investment. In this market, the investors are looking for the best securities to maximize the profit. Therefore, forecasting the stock price of next day has a vital role in purchasing such securities. To do this, app MoreStock exchange market is one of the important ways to investment. In this market, the investors are looking for the best securities to maximize the profit. Therefore, forecasting the stock price of next day has a vital role in purchasing such securities. To do this, application of Neural Networks financial forecasting has become very popular over the last few years. In this paper, for predicting the next day's close stock price of Pars Petroleum Company, Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) will be developed, used and compared. The data are daily collected and analyzed during 2009-2011. The findings indicate that forecasting the price by Neural Network is superior to ARIMA due to its less error coefficients and high explanatory ability. Manuscript profile -
Open Access Article
20 - Improving the Performance of Forecasting Models with Classical Statistical and Intelligent Models in Industrial Productions
Maryam Bahrami Mehdi Khashei Atefeh Amindoust -
Open Access Article
21 - Monitoring annual precipitation changes in Dezful plain with statistical analysis and time series
Yaser Sabzevari Saeid Eslamian Keyvan Moradalivand -
Open Access Article
22 - Trends in extreme daily temperature in south west of Iran during recent decades
alireza shakiba einallah khalili amaneh dashtbozorgiLong-term annual or seasonal averages of climatic variables like temperature, has generally been used as indicator for the assessment of climate change. Accordingly, this paper is aimed to examine extreme temperature as indicative climatic variable in synoptic sta MoreLong-term annual or seasonal averages of climatic variables like temperature, has generally been used as indicator for the assessment of climate change. Accordingly, this paper is aimed to examine extreme temperature as indicative climatic variable in synoptic stations of Ahwaz located in Khozestan province during a 40 year period (1964- 2003) to determine recent climatic changes over the mentioned area. To achieve the aim, 6 indices of extreme temperature recommended by the internationally agreed WMO–CCL/CLIVAR list of over 50 climate change indices climate extremes were selected and calculated. The six indices are consist of cold nights, cold days, warm nights, hot days, summer days and diurnal temperature range. These indices describe cold extremes as well as warm extremes. Extreme temperature indices for this study are based on the 1st and 99th percentiles. According to the current climate condition, based on ARIMA model, 10 years prediction of mentioned indices also was made. The results of the research indicated that thermal conditions over the area are changing as indicated by a warming trend identified during the study period. The results also shown the marked negative trends for indices such as cold days, cold nights and diurnal temperature range over the region. While significant increases are detected in the annual number of hot days, warm nights and summer days. The output of ARIMA model confirmed consist warming trend for Ahwaz in the next 10 years (2004-2013). Manuscript profile -
Open Access Article
23 - Forecasting Iran’s Rice Imports during 2009-2013
Mohammad Reza Pakravan Mohammad Kavoosi Kelashemi Hamid Reza AlipourIn the present study Iran’s rice imports trend is forecasted, using artificial neural networks and econometric methods, during 2009 to 2013, and their results are compared. The results showed that feet forward neural network leading with less forecast error and ha MoreIn the present study Iran’s rice imports trend is forecasted, using artificial neural networks and econometric methods, during 2009 to 2013, and their results are compared. The results showed that feet forward neural network leading with less forecast error and had better performance in comparison to econometric techniques and also, other methods of neural networks, such as Recurrent networks and Multilayer perceptron networks. Moreover, the results showed that the amount of rice import has ascending growth rate in 2009-2013 and maximum growth occurs in 2009-2010 years, which was equal to 25.72 percent. Increasing rice import caused a lot of exchange to exit out of the country and also, irreparable damage in domestic production, both in terms of price and quantity. Considering mentioned conditions, economic policy makers should seek ways to reduce increasing trend of rice import; and more investment and planning for domestic rice producers. Manuscript profile -
Open Access Article
24 - Future Prospects of Iran, U.S and Turkey's Pistachio Exports
Mohammad Reza Pakravan Mohammad Kavoosi KalashamiIn this study, the situation of Iran, U.S and Turkey's Pistachio export is investigated. to this purpose, Revealed Comparative Advantage (RCA) Index is calculated based on Agricultural and total economy export, separately, then forecasted by using Auto- Regressive Integ MoreIn this study, the situation of Iran, U.S and Turkey's Pistachio export is investigated. to this purpose, Revealed Comparative Advantage (RCA) Index is calculated based on Agricultural and total economy export, separately, then forecasted by using Auto- Regressive Integrated Moving Average (ARIMA) approached, for 2008-2013. The results show that considering both commodity baskets, Turkey and Iran had comparative advantage in Pistachio export in 1982-2007, but U.S did not. Also, forecasting RCA index, based on both commodity baskets, show the improvement of U.S Pistachio export situation, unlike the values of RCA index forecasting for Iran and Turkey is falling. Therefore, it is recommended that Iran and Turkey attempt to identify new consumer markets in order to retain their market shares in pistachio export. Following the U.S imposed policies during last six years which improved its pistachio export, Iran and Turkey can increase their market shares. Manuscript profile -
Open Access Article
25 - Validation a scale for measuring entrepreneurship of managers in governmental organizations
Nasrin Sohrabifard Ali Akbar Khosravi Heidar Ali Hoomanthis study attempted to devise and validate a scale for measuring entrepreneurship among managers of the Tehran area’s governmental organizations. A random of Iran, participated in this research. In the first stage, Morethis study attempted to devise and validate a scale for measuring entrepreneurship among managers of the Tehran area’s governmental organizations. A random of Iran, participated in this research. In the first stage, a -item questionnaire scale from one to four was used. The results of this stage of the study showed that the Cronbach’s alpha coefficient ! d the item-total Polyserial correlations of seven items were not significant. In " lacked significant factor loading were omitted from the # $ reliability %& & analysis, using a varimax rotation, resulted in the following components: endeavor risk taking, locus of control, fluency, creativity and innovation, flexibility, and uncertainty and ambiguity tolerance. Manuscript profile -
Open Access Article
26 - The Application of GMDH Neural Network Approachin Forecasting the Price of Soybean Meal in Merchendis Stock Exchange
علی اکبر باغستانی سعید یزدانی مجید احمدیانAbstract Livestock and poultry industry has depended much on soybean meal. This dependence has led to fluctuations in the price of this product and therefore, forces market participants to follow the sensitivity and accuracy. These fluctuations created serious concerns MoreAbstract Livestock and poultry industry has depended much on soybean meal. This dependence has led to fluctuations in the price of this product and therefore, forces market participants to follow the sensitivity and accuracy. These fluctuations created serious concerns about the supply and price of soybean meal. So, this study, using monthly and weekly data of Soybean prices in the exchange market, tried to forecast soybean price. So Soybean Meal price has predicted with neural network GMDH algorithm and ARIMA. The results based on the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MPAE) showed that the GMDH algorithm, has a better ability to predict the price accurately. Manuscript profile -
Open Access Article
27 - تأثیر نااطمینانی تولید ناخالص داخلی و تورم بر منابع و مصارف بانک ملی ایران
جواد صلاحی سید رضا خادمی -
Open Access Article
28 - پیشبینی قیمت بنزین فوب خلیجفارس با استفاده از مدلهای ARIMA و ARFIMA
حمید آماده فرشید عفتی باران امین امینی -
Open Access Article
29 - An attempt to forecast employment by economic sectors: 4141 (0402)
Shahriar Nessabian Saleh GhavidelForecasting employment and unemployment figures for Iran can help depict labor market condition on horizon of vision plan. Using ARIMA model, the author has aimed to forecast the share of economic sectors, namely agriculture, industry and mines and service sector, in Ir MoreForecasting employment and unemployment figures for Iran can help depict labor market condition on horizon of vision plan. Using ARIMA model, the author has aimed to forecast the share of economic sectors, namely agriculture, industry and mines and service sector, in Iran’s employment over the period of 6009-6062. The findings of this paper indicate that over the past 20 years (6723-6003) employment trends have not been uniform for these sectors, i.e., it has been decreasing for agriculture and increasing at a slow rate for industry and mines. For service sectors it has been constantly increasing. The findings further provide the ground to argue that this pattern of developments in employment would be maintained on the horizon of vision plan in 6101(6062) Manuscript profile -
Open Access Article
30 - پیشبینی قیمت جهانی گندم و صرفهجویی ارزی در ایران
شهریار نصابیان شهاب الدین قشقایی -
Open Access Article
31 - Flood Water Surface Profile in Tapi River- Surat
G. I. Joshi A. S. Patel -
Open Access Article
32 - Stock Price Prediction in Tehran Stock Exchange Using Artificial Neural Network Model and ARIMA Model: A Case Study of Two Active Pharmaceutical Companies in Stock Exchange
Ahmad Chegeni AZIZ GORDIn This Study We Compare the Efficiency of Both Artificial Neural Network Prediction Methods (ANN) and Traditional Method of Auto Regressive Integrated Moving Average (ARIMA) in Predicting Stock Prices in Iranian Stock Market. For This Purpose, Four Pharmaceutical Compa MoreIn This Study We Compare the Efficiency of Both Artificial Neural Network Prediction Methods (ANN) and Traditional Method of Auto Regressive Integrated Moving Average (ARIMA) in Predicting Stock Prices in Iranian Stock Market. For This Purpose, Four Pharmaceutical Companies, Alborz Drug, Iran Drug, Pars Drug, and Jam Drug Were Selected and ARIMA Model and Artificial Neural Network Model Were Estimated For All Four Companies. In Order to Estimate Artificial Neural Network Model, Stock Price Variable as Dependent Variable and Stock Trading Volume, Drug Industry Index, OPEC Oil Price, Exchange Rate and Gold Price are Considered as Independent Variables. MSE, RMSE, MAD, R2 and MAPE Criteria Were Used to Compare Two Models. In Order to Estimate the Stock Price Forecast Regression Model, Use of Auto Regressive Integrated Moving Average (ARIMA) Regression Is Used and Estimation of the Coefficients of the Model is Performed Using the EVIEWS Statistical Software. An Suitable ANN Model Was Created For Predicting Stock Prices Using MATLAB Software. The Results of the Research Showed That the Research Hypothesis is Correct and the Artificial Neural Network Model (ANN) Has a Better Predictor of Stock Price in the Iranian Stock Market Than the ARIMA Method. Manuscript profile -
Open Access Article
33 - The assessment of extreme value theory and Copula - Garch models in prediction of value at risk and the expected short fall in portfolio Investment Company in Tehran stock exchange.
ali alizadeh Mirfeiz FallahThe present study has endeavored to represent a more precise model to calculate the risk of banks in this study by ARIMA-GARCH-COPULA Model has been introduced.In obtaining the iid distributions and variance estimation the mean model and conditional variance have been d MoreThe present study has endeavored to represent a more precise model to calculate the risk of banks in this study by ARIMA-GARCH-COPULA Model has been introduced.In obtaining the iid distributions and variance estimation the mean model and conditional variance have been determined and estimated simultaneously.In so doing, the ARIMA methodology has been employed to model the average return on assets of the study, and for modeling the research conditional variance of GARCH have been applied. Also mean error criterion has been used to compare the different models of VAR estimation, and for the purpose of testing statistical results backtesting methods have been employed. Based on mean error criterion, the proposed model of the study at hand has demonstrated the most accuracy The GEV model derived from the EVT has been ranked second The output of the Dow ranking method, however, has been very similar to one another According to Dow ranking method, the GEV model has had the lowest loss function at 5% level of significance, and at 1% level of significance, the HS model has demonstrated the least loss function. ES calculations have also been carried out for the four models with ARIMA-GARCH-COPULA model showing the least loss. Manuscript profile -
Open Access Article
34 - Predicting Capital Market Returns Using the Learning Model of Levenberg-Marquardt, Gradient descent and ARIMA Algorithm
mehdi asharion ghomizadeh mohammad mahmoodiThe present study compares and predicts the predictive ability of the capital market based on the learning pattern of the Levenberg-Marquardt algorithm, the Gradient descent and the ARIMA Algorithm. For this purpose, market data were used in the period from 1394 to 1397 MoreThe present study compares and predicts the predictive ability of the capital market based on the learning pattern of the Levenberg-Marquardt algorithm, the Gradient descent and the ARIMA Algorithm. For this purpose, market data were used in the period from 1394 to 1397, and more than 75% of these data were used as training data prior to 1397, and one year end data were used as data. The results of the evaluation of the research data show that artificial neural networks have a high capacity for price prediction.The results also showed that in both training data series from 1394 to 1396 and experimental of 1397 the comparison of the results and performance of ARIMA neural networks (ARIMA) showed that the neural network had higher predictive power in Comparing with the performance and prediction accuracy of two types of neural networks with the Levenberg-Marquardt learning algorithm and the Gradient descent learning algorithm using the Levenberg-Marquardt learning algorithm has been able to increase the neural network prediction accuracy And reduce its error, so, the results of the present study show, the Levenberg-Marquardt learning algorithm improves the predictive power of the neural network. Manuscript profile -
Open Access Article
35 - Estimation of a model for predicting the trend of digital currencies (Bitcoin, Ethereum) in the corona and post-corona periods with the help of time series
Seyed Ramin Saeedi nezhad sina lalehAfter the broadcast world and the epidemic of pandemic covid-19 was a severe economic crisis, For this reason, the need for more prediction became apparent. One of these methods is time series prediction. In this study, first, the effect of covid-19 disease on price of MoreAfter the broadcast world and the epidemic of pandemic covid-19 was a severe economic crisis, For this reason, the need for more prediction became apparent. One of these methods is time series prediction. In this study, first, the effect of covid-19 disease on price of Ethereum and Bitcoin, and the results show that this disease had a negative effect on world prices of Ethereum and Bitcoin. In the next step, using univariate time series methods and with the help of ARIMA models, a model for predicting which is the best model AR (1) and MA(1) and time differentiation was designed, the one-year and two-year forecasts were done with the designed model. According to the reports of the World Health Organization, there is probably corona pandamic for up to one year, and For the next two years, Corona has emerged from a pandemic is called the post-corona period. The results show that After a short decline and reacting to resistance and support, they will have an annual upward trend. Manuscript profile -
Open Access Article
36 - Using Brownian motion in stock prices prediction in comparison with ARIMA
farhad karimiasl ali saeydy heidar foroghneghad mohammad kodaei voleh zaghrdThe main reason that people invest in the stock market is to earn profits that require having accurate market information and stock changes and predicting its future trend. Therefore, the investor needs the powerful and reliable tools needed to predict stock prices. In MoreThe main reason that people invest in the stock market is to earn profits that require having accurate market information and stock changes and predicting its future trend. Therefore, the investor needs the powerful and reliable tools needed to predict stock prices. In this regard, the present study investigates stock price forecasts based on MSE mean square error, mean absolute deviation MAE and root mean square error RMSE. Finally, the methods investigated in this study are compared and identify the top method to predict stock prices. For this purpose, the data of the top 50 stock exchange companies, which are quarterly presented by the stock exchange organization, were used during the period 2012-2018. In order to test the research hypotheses, linear regression method, Brownian method and ARIMA method were used. The research findings show that the Brownian model predicts stock prices more accurately than the ARIMA method. It was also observed that linear statistical ARIMA models are less efficient in the financial markets than the brownian methods. Manuscript profile -
Open Access Article
37 - Forecasting the bank's financial resources using the linear model (ARIMA) and nonlinear artificial fuzzy networks
omid mehrinamakawarani reza ehteshamrasiOne of the most important issues of banking managers as an influential variable on the banking industry is the knowledge of the status of bank deposits that the bank depends on a large extent on it. Therefore, bank managers are keen to know how much the total bank depos MoreOne of the most important issues of banking managers as an influential variable on the banking industry is the knowledge of the status of bank deposits that the bank depends on a large extent on it. Therefore, bank managers are keen to know how much the total bank deposits will be at a given time in the future. Predicting the amount of deposits, changes and fluctuations of these deposits can help banks in planning and decision making. In this research, using statistical techniques and approach of artificial neural network models, we have tried to introduce a model with the highest estimation power and the least amount of error to predict the amount of deposits or the same sources of finance by their different types for the desired bank. To test the hypotheses, one private bank information was used during the period of 1387-1396. In this research, we compared the predictive power of ARIMA and artificial neural network method. To assess the accuracy of forecasting the bank's resources, the ARIMA method used Coopiff and Christopherson tests. The results of the research on the amount of bank deposits monthly showed that the neural network method provides better estimates than the ARIMA method. Manuscript profile -
Open Access Article
38 - ارزیابی توان پیشبینی سود فصلی هر سهم بااستفاده ازمدلهای سری زمانی
حسین اعتمادی علی اصغر انواری رستمی وحید احمدیان -
Open Access Article
39 - مقایسه عملکرد مدلهای رگرسیونی ARIMA وشبکه عصبی باالگوریتم ژنتیک (GMDH) درپیش بینی قیمت نفت خام ایران
عباسعلی ابونوری ناهید خدادادی -
Open Access Article
40 - Interval Forcasting for Gold Price with hybrib model of ARIMA and Artificial Neural Network
Shapor Mohammadi Reza Raeie Mohammadreza RahimiPrice forecasting is one of the most challenging issues that the speculators, traders and brokers are faced with. On the other hand in interval analysis it is supposed that observations and estimations in the real world are not complete and reliable so to increase the a MorePrice forecasting is one of the most challenging issues that the speculators, traders and brokers are faced with. On the other hand in interval analysis it is supposed that observations and estimations in the real world are not complete and reliable so to increase the accuracy we should describe the data as the intervals that includes real quantities. Various methods are used in order to model the time series such as price. Autoregressive integration moving average (ARIMA), which is known as box-Jenkins method is one of the most commonly used models in forecasting of time series during the past three decades. But the main assumption is that there is a linear relationship between the values of the series therefore nonlinear relationships cannot be explained completely by using autoregressive integration moving average (ARIMA). Another method in time series forecasting is neural network which can estimate the various nonlinear relationship (called neural network universal estimating) but according to the literature, using network will have complicated results. Since it is difficult to understand the linear and nonlinear data pattern in reality, this idea will come to mind that the combination of linear and nonlinear models could increase the accuracy of forecasting. So in this research the linear part will be estimated by ARIMA and then the non-linear residuals will be modeled by neural network and finally the predicted result will be added to ARIMA in order to forecast the low, high and close price of gold .comparing the accuracy of the hybrid model to ARIMA and neural network by pair compared, Diebold-Mariano and Harvey-Newbold –Leybourn test and two criteria (MSE and MAE) showed that the hybrid model presented better performance. Manuscript profile -
Open Access Article
41 - بررسی جایگاه تیم ملی فوتبال ایران در رده بندی فدراسیون جهانی: مدل پیش بینی ANN و ARIMA
مهوش نوربخش امیر سرشین سردار محمدی
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