Different methods for gas price forecasting

The difficulty in gas price forecasting has attracted much attention of academic researchers and business practitioners. Various methods have been tried to solve the problem of forecasting gas 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) employs for gas price forecasting. In this paper we use a new method for extract the rules and compare different methods for gas price forecasting. Our research reveals that during the recent financial crisis period by employing hybrid intelligent framework for gas price forecasting, we obtain better forecasting results compared to the GMDH neural networks and MLF neural networks and results will be so better when we employ hybrid intelligent system with for gas price volatility forecasting.