Good Prediction of Gas Price between MLFF and GMDH Neural Network

Studies show neural networks have better results in predicting of financial time series in comparison to any linear or non-linear functional form to model the price movement. Neural networks have the advantage of simulating the non-linear models when little a priori knowledge of the structure of problem domains exist or the number of immeasurable input variables are great and system has a chaotic characteristics. Among different methods, MLFF neural network with back-propagation learning algorithm and GMDH neural network with genetic learning algorithms are used to predict gas price of the Henry Hob database covering 01/01/2004-13/7/2009 period. This paper uses moving average crossover inputs and the results confirms (1) the fact there is short-term dependence in gas price movements, (2) the EMA moving average has better result and also (3) by means of the GMDH neural networks, prediction accuracy in comparison to MLFF neural networks, can be improved.