خلاصة:
he difficulty in gasoline price forecasting has attracted much attention of academic researchers and business practitioners.Various methods have been tried to solve the problem of forecastinggasoline prices however, all of the existing models of prediction cannot meet practical needs. In this paper, a novel hybrid intelligent framework is developed by applying a systematic integration of GMDH neural networks with GA and Rule-based Exert System (RES) with Web-based Text Mining (WTM) employs for gasoline price forecasting. Our research reveals that during the recent financial crisis period by employing hybrid intelligent framework for gasoline price forecasting, we obtain better forecasting results compared to the GMDH neural networks and results will be so better when we employ hybrid intelligent system with GARCH (1, 1) for gasoline price volatility forecasting.
ملخص الجهاز:
In this paper, a novel hybrid intelligent framework is developed by applying a systematic integration of GMDH neural networks with GA and Rule-based Exert System (RES) with Web-based Text Mining (WTM) employs for gasoline price forecasting.
Gasoline price forecasting; Web-based Text Mining (WTM); Group Method of Data Handling (GMDH) neural networks; Genetic Algorithm (GA); Hybrid Intelligent System; Rule–based Expert System (RES); GARCH (1, 1) method 1- Introduction Problems of complex objects modeling such as analysis and prediction of stock market, gasoline price and other such variables cannot be solved by deductive logical-mathematical methods with needed accuracy with a ∗ Professor; Faculty of Economics University of Tehran.
(2008) used GMDH neural network based on Genetic Algorithm to model and forecast the price of Gasoline by using two approaches; Deductive Method and Technical Analysis.
(2008) used a GMDH neural network model with moving average crossover inputs to predict price in the crude oil futures market.
In the simulation study, we reveal that forecasting rules from expert system and moving average gasoline price are modeled by using GMDH neural networks.
We observed that during the crisis period, when we investigate the effects of irregular and infrequent events on gasoline price by WTM and RES, we obtain better forecasting results compared to the GMDH neural networks and results will be so better when we employ hybrid intelligent system with GARCH (1, 1) for gasoline price volatility forecasting.