Combining Time-Scale Feature Extractions with SVMs for Stock Index Forecasting
Support vector machine (SVM) has appeared as a powerful tool for time series forecasting and demonstrated better performance over other methods. This paper proposes a novel hybrid model which combines time-scale feature extractions with SVM models for stock index forecasting. The time series of explanatory variables are decomposed by the wavelet basis, and the extracted time-scale features then serve as inputs of a SVM model which performs the nonparametric forecasting. Compared with pure SVM models or traditional GARCH models, the performance of the new method is the best. The root-mean-squared forecasting errors are significantly reduced. The results of this study can help investors for controlling and reducing their risks in international investments.
KeywordsSupport Vector Machine Wavelet Analysis Support Vector Machine Model Stock Index Reproduce Kernel Hilbert Space
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