Forecasting crude oil prices using wavelet ARIMA model approach

Abstract

The increase in crude oil prices can give a significant impact on worldwide economic activities. Therefore, an accurate forecasting is crucial so that necessary precaution steps can be planned earlier. This paper proposed a Wavelet-ARIMA approach to improve the forecasting accuracy of the crude oil price series. In this approach, wavelet transform decomposes the original series into a more stable constitutive series while ARIMA model is utilized to forecast each individual series and lastly inversed back to original series. Dataset of crude oil spot prices from West Texas Intermediate and the Brent North Sea are used. Direct application of ARIMA model is used as a benchmark for effectiveness measurement with the proposed approach by comparing Mean Absolute Error and Root Mean Square Error obtained. R software is used to implement ARIMA while MATLAB is used for dataset pre-processing. The result has proven that Wavelet-ARIMA approach demonstrates better prediction performance than ARIMA model.