Time series forecasting by a seasonal support vector regression model

The support vector regression (SVR) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the applications of SVR models in a seasonal time series forecasting has not been widely investigated. This study aims at developing a seasonal support vector regression (SSVR) model to forecast seasonal time series data. Seasonal factors and trends are utilized in the SSVR model to perform forecasts. Furthermore, hybrid genetic algorithms and tabu search (GA/TS) algorithms are applied in order to select three parameters of SSVR models. In this study, two other forecasting models, autoregressive integrated moving average (SARIMA) and SVR are employed for forecasting the same data sets. Empirical results indicate that the SSVR outperforms both SVR and SARIMA models in terms of forecasting accuracy. Thus, the SSVR model is an effective method for seasonal time series forecasting.

[1]  Guoqiang Peter Zhang,et al.  Neural network forecasting for seasonal and trend time series , 2005, Eur. J. Oper. Res..

[2]  E. Bompard,et al.  Price forecast in the competitive electricity market by support vector machine , 2007 .

[3]  Kyungdoo “Ted” Nam,et al.  FORECASTING INTERNATIONAL AIRLINE PASSENGER TRAFFIC USING NEURAL NETWORKS , 1995 .

[4]  Aun-Neow Poo,et al.  Support vector regression model predictive control on a HVAC plant , 2007 .

[5]  Rob Law,et al.  Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. , 2002 .

[6]  Zhongsheng Hua,et al.  A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts , 2006, Appl. Math. Comput..

[7]  Wei-Zhen Lu,et al.  Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends. , 2005, Chemosphere.

[8]  J. Mercer.  Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations , 1909 .

[9]  Paul A. Fishwick,et al.  Feedforward Neural Nets as Models for Time Series Forecasting , 1993, INFORMS J. Comput..

[10]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[11]  Mohamed Mohandes,et al.  Support vector machines for wind speed prediction , 2004 .

[12]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[13]  Wei-Chiang Hong,et al.  An Improved Neural Network Model in Forecasting Arrivals , 2005 .

[14]  L. Papageorgiou,et al.  Customer Demand Forecasting via Support Vector Regression Analysis , 2005 .

[15]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[16]  W. N. Venables,et al.  Statistical forecasting of soil dryness index in the southwest of Western Australia , 2003 .

[17]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[18]  Ping-Feng Pai,et al.  Potential assessment of the support vector regression technique in rainfall forecasting , 2007 .

[19]  Jen-Hung Huang,et al.  Earthquake devastation and recovery in tourism: the Taiwan case , 2002 .

[20]  Peter M. Williams,et al.  Modelling Seasonality and Trends in Daily Rainfall Data , 1997, NIPS.

[21]  I-Fan Chang,et al.  Support vector regression for real-time flood stage forecasting , 2006 .

[22]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[23]  Fred W. Glover,et al.  Genetic algorithms and tabu search: Hybrids for optimization , 1995, Comput. Oper. Res..

[24]  Francis Eng Hock Tay,et al.  Modified support vector machines in financial time series forecasting , 2002, Neurocomputing.

[25]  J. Navarro-Esbrí,et al.  Time series analysis and forecasting techniques for municipal solid waste management , 2002 .

[26]  Shouyang Wang,et al.  Forecasting stock market movement direction with support vector machine , 2005, Comput. Oper. Res..