Least square support vector machines as an alternative method in seasonal time series forecasting

Abstract

The least square support vector machines (LSSSVM) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the applications of LSSVM model in a seasonal time series forecasting has not been widely investigated. This study aims at developing a LSSVM model to forecast seasonal time series data. To assess the effectiveness of this model, the airline passenger series exhibits nonlinear behaviour and shows multiplicative seasonal behaviour was applied. In order to obtain the optimal model parameters of the LSSVM, a grid search algorithm and cross-validation method were employed. In this study, seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) models are employed for forecasting the same data sets. Empirical results indicate that the LSSVM yields well forecasting performances. Thus, the LSSVM model provides a promising alternative for seasonal time series forecasting.