Hybrid Method Based on Wavelet Transformation and Reinforcement Learning To Forecast Crude Oil Price

Abstract

Crude oil prices are usually not stationary and affected by several factors that affect supply and demand, so the process of estimating and forecasting the price is not clear. By relying on artificial intelligence, we can improve the prediction process. In the proposed research, the original data are processed using a moving average, which is one of the time series techniques used in forecasting processes and then use both reinforcement learning and wavelet transformation to perform improvements on the moving average method specialized in future forecasting. Where the optimization method is accomplished in two stages. First, Prices decomposition by Haar that one of the algorithms of the wavelet transformation, and the second stage is performed by Q-Learning, one of the most common reinforcement learning methods. By comparison between the results of the previous research and the proposed research, the results of hybrid methods are more accurate and effective than the other method research. Whereas, hybrid technologies take advantages of the combined methods.