Combining KPCA with support vector machine for time series forecasting

Recently, support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecastor, the first important step is feature extraction. This paper applies kernel principal component analysis (KPCA) to SVM for feature extraction. KPCA is a nonlinear PCA developed by using the kernel method. It firstly transforms the original inputs into a high dimensional feature space and then calculates PCA in the high dimensional feature space. By examining the sunspot data and one real futures contract, the experiment shows that SVM by feature extraction using KPCA performs much better than that without feature extraction. In comparison with PCA, there is also superior performance in KPCA.