A Hybrid Intelligent System for Forecasting Gasoline Price. A Comparative Approximate Economic

The 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 forecasting gasoline 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.