FORECASTING CRUDE OIL PRICE USING ARIMA AND FACEBOOK PROPHET WITHI MACHINE LEARNING - Mukhriz Izraf Azman, Aziz; Mohamad Hardyman, Barawi (2021)
Oil price forecasting has received a great deal of attention from practitioners and researchers alike, but it remains a difficult topic because of its dependency on a variety of factors, including the economic cycle, international relations, geopolitics, and so on. Forecasting the price of oil is a difficult but gratifying task. Motivated by this issue, we present a robust model for accurate crude oil price forecasting using ARIMA and PROPHET models based on machine learning technique to produce a reliable weekly and monthly crude oil price predictions. We apply the Savitzky Golay smoothing filter to get a better denoising performance for our forecast models. For model evaluation, we apply cross validation with sliding windows on both models and compares the performances using RMSE and MAPE. The results shows that the ARIMA- based machine learning approach performs better as compared to the PROPHET model for both one-week and one-month forecast ahead intervals.