SMO Algorithm Applied in Time Series Model Building and Forecast

Abstract

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.