SMO ALGORITHM APPLIED IN TIME SERIES MODEL BUILDING AND FORECAST

摘要：

As a novel learning machine, the support vector machine (SVM) based on statistical learning theory can be used for regression: support vector regression (SVR).SVR has been applied successfully to time-series analysis, but its optimization algorithm is usually built up from certain quadratic programming (QP) packages.Therefore, for small datasets this is practical and QP routines are the best choice, but for large datasets, data processing runtimes become lengthy, which limits its application.Sequential minimal optimization (SMO) algorithm can improve operation speed and reduce this long runtime.In this paper, SVR that is based on the SMO algorithm is used to forecast two typical time series models: Wolfer sunspot number data and Box and Jenkins gas furnace data.The results of simulation prove that the operational speed of SVR using the SMO algorithm is improved in comparison to SVR employing QP optimization algorithm; moreover, the forecasting precision is better than that of neural network and SVR using QP optimization algorithm.

关键词： Model analysis and forecast Time series Sequential minimal optimization (SMO) algorithm Support vector regression Support vector machine

作者: JIN-FANG YANG YONG-JIE ZHAI DA-PING XU PU HAN

作者单位: Department of Automation, North China Electric Power University, Baoding 071003, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 2395-2400

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）