Financial Time Series Forecasting Using a Compound Model Based on Wavelet Frame and Support Vector Regression

In this paper, a financial time series forecasting model based on wavelet frame and support vector regression is proposed. In the proposed model, wavelet frame is first used to decompose the predicting variables into sub-series with different scales. The hidden information of the predicting variables could be discovered in these sub-series. The SVR then uses the sub-series to build the forecasting model. In order to evaluate the performance of the proposed approach, the Nikkei 225 opening cash index is used as the illustrative example. The experimental results show that the proposed model outperforms the SVR model and random walk model.