Financial Time Series Forecasting with Grouped Predictors using Hierarchical Clustering and Support Vector Regression

Abstract

Financial time series prediction is regarded as one of the most challengingtasks due to the inherent noise and non-stationality of the data. This paperproposed a two-stage financial time series prediction approach hybridizingsupport vector regression (SVR) with hierarchical clustering (HC). By averaging the variables within the clusters obtained from hierarchical clustering, we define super predictors and use them as the input variables of the SVRforecasting model. Although averaging is a simple technique, it plays animportant role in reducing variance. To evaluate the performance of theproposed approach, the Shanghai-Shenzhen 300 index is used as illustrativeexample. The experimental results show that the proposed approach outperforms both the SVR model with principal component analysis and the SVRmodel with genetic algorithms in average prediction error and predictionaccuracy.