Forecasting Crude Oil Prices Using Wavelet Neural Networks
نویسندگان
چکیده
According to International Energy Outlook 2007 the total world demand of energy is projected to increase through 2030 about 95% for the non-OECD region and 24% for OECD nations. Crude oil is one of the most critical energy commodities while with coal and natural gas are projected to provide roughly the 86% share of the total US primary energy supply in 2030. In this paper, we use wavelet neural networks to forecast monthly West Texas Intermediate (WTI) crude oil spot prices. As explanatory variables we consider price lags, the producer price index for petroleum and the world production of crude oil. The data are provided by the Energy Information Administration (EIA). The proposed model is used to forecast in-sample and out-of-sample. We forecast one, three and six month future prices of crude oil and we compare our estimates with the EIA’s STEO econometric forecasting
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