Least square support vector machines as an alternative method in seasonal time series forecasting
Applied Mathematical Sciences
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
... 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. been devoted over the past several decades to the development and improvement of time series forecasting models. One of the best-known approaches in the development of time series model is the seasonal auto-regressive integrated moving average (SARIMA) model. The SARIMA model is one of the most popular approaches in seasonal time series forecasting owing to its statistical properties, as well as the well-known Box-Jenkins methodology used for constructing the model. The SARIMA model has been successfully utilized in many fields of forecasting such as in economic, engineering, foreign exchange, stock market and social  . Although the SARIMA model has been highly successful in both academic research and industrial application during the past three decades, it suffers from a major limitation owing to its pre-assumed linear form of the model. Recently, artificial neural network (ANN) model has been extensively studied and also used as an alternative in forecasting seasonal data pattern  . Some literatures indicated that ANN can obtain desirable results in seasonal and trend forecasting [4, 11, 15] . While some researchers claim that ANN is unsuccessful in finding out seasonal effect in the data structure [6, 9, 17] . Suykens et al.  proposed a modified version of support vector machines (SVM) called least squares support vector machines (LSSVM). In recent years, LSSVM extended to cope with forecasting problems, and has been used successfully in various areas of pattern recognition and time series forecasting problems [5, 7, 13, 16] . However, applications of the LSSVM models in seasonal time series data have not been widely studied. Therefore, this study attempts to develop a LSSVM model in the seasonal time series forecasting problems.