Time series forecasting by a seasonal support vector regression model

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题名:	Time series forecasting by a seasonal support vector regression model
作者:	Pai, P.-F.a , Lin, K.-P.b , Lin, C.-S.c , Chang, P.-T.c
贡献者:	Department of Industrial Engineering and Enterprise Information, Tunghai University
关键词:	Forecast;Seasonal autoregressive integrated moving average;Seasonal time series;Support vector regression
日期:	2010
上传时间:	2013-05-15T09:09:20Z (UTC)
摘要:	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. ? 2009 Elsevier Ltd. All rights reserved.
關聯:	Expert Systems with Applications Volume 37, Issue 6, June 2010, Pages 4261-4265
显示于类别:	[工業工程與經營資訊學系所] 期刊論文

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