SD-LSSVR-based Decomposition-and-Ensemble Methodology with Application to Hydropower Consumption Forecasting

摘要：

Due to the distinct seasonal characteristics of hydropower,this study tries to propose a seasonal decomposition (SD) based least squares support vector regression (LSSVR) ensemble learning model for hydropower consumption forecasting.In the SD-LSSVR-based decomposition and ensemble model,the original hydropower consumption series are first decomposed into trend cycle, seasonal factor and irregular component.Then the LSSVR is used to predict the three different components independently. Finally,these prediction results of the three components are combined with another LSSVR to formulate an ensemble result as the final prediction.Experimental results reveal that the proposed novel method is very promising for time series forecasting with seasonality and nonlinearity for that it outperforms all the other benchmark methods listed in our study in both level accuracy and directional accuracy.

关键词： Hydropower consumption forecasting Seasonal decomposition LSSVR ensemble learning

作者: Shuai Wang Ling Tang Lean Yu

作者单位: Institute of Policy and Management,Chinese Academy of Sciences Beijing 100190,China,Graduate Univers Institute of Policy and Management,Chinese Academy of Sciences Beijing 100190,China,Graduate Univer Institute of Systems Science,Academy of Mathematics and Systems Science,Chinese Academy of Sciences

会议类型: 国际会议

会议名称: The Fourth International Joint Conference on Computational Science and Optimization(第四届计算科学与优化国际大会 CSO 2011)

会议地点: 昆明、丽江

会议语种:英文

页码: 603-607

在线出版日期: 2011-04-15（万方平台首次上网日期，不代表论文的发表时间）