A comparison of time series forecasting using support vector machine and artificial neural network model

Abstract

Time series prediction is an important problem in many applications in natural science, engineering and economics. The objective of this study is to examine the flexibility of Support Vector Machine (SVM) in time series forecasting by comparing it with a multi-layer back-propagation (BP) neural network. Five well-known time series data sets are used in this study to demonstrate the effectiveness of the forecasting model. These data are utilized to forecast through an application aimed to handle real life time series. The grid search technique using 10-fold cross validation is used to determine the best value of SVM parameters in the forecasting process. The experiment shows that SVM outperforms the BP neural network based on the criteria of Mean Absolute Error (MAE). It also indicates that SVM provides a promising technique in time series forecasting techniques.