Knowledge about future oil demand is essential for OPEC member countries to set priorities and select policies in order to achieve economic growth and development. So in this study, the OPEC oil demand has been predicted using time series models Including Structural Vec More
Knowledge about future oil demand is essential for OPEC member countries to set priorities and select policies in order to achieve economic growth and development. So in this study, the OPEC oil demand has been predicted using time series models Including Structural Vector Autoregressive model (SVAR), Autoregressive Integrated Moving Average model (ARIMA) and Gravitational Search Algorithm (That is one of the Innovative Search Algorithms) applying demand data from 1970 to 2014. In this regard, three criteria including Mean Sum of Squared Errors (MSSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) have been used to measure the predictive power of triple models. Results indicate that the SVAR model has the most appropriate prediction of OPEC global demand. According to results of this model, net export variable has a positive and significant impact on oil demand and OPEC petroleum price and non- OPEC production variables have a negative and significant impact on oil demand.
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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.
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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.
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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.
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