The Combination of Autoregressive Integrated Moving Average (ARIMA) and Support Vector Machines (SVM) for Daily Rubber Price Forecasting

Abstract

Natural rubber (NR) price is easily affect by the long term and short term exchange rate of developments on supply and demand sides, as well as the effects of exchange rates. Due to the fact that monthly, quarterly, and annually data have underwent the smoothing technique, it may has missed out some of the important characteristics or information describing the rubber price. Since the NR price is changing daily, therefore, this study focuses on the predicting the future daily prices. A combination models of Autoregressive Integrated Moving Average (ARIMA) and Support Vector Machine (SVM) is proposed in order to capture the future value of NR prices. The experimental results show that the proposed model performs the best whereby it has under predicted by 6.31% with the r value of 0.9976 compared to single ARIMA and SVM models. As the results, the combination model shows to be an effective tools in improving the forecasting accuracy by reducing the model forecast error.