Automatic optimized support vector regression for financial data prediction

The aim of this article is to introduce a hybrid approach, namely optimal multiple kernel–support vector regression (OMK–SVR) for time series data prediction and to analyze and compare its performances against those of support vector regression with a single RBF kernel (RBF-SVR), gene expression programming (GEP) and extreme learning machine (ELM) on the financial series formed by the monthly and weekly values of Bursa Malaysia KLCI Index, monthly values of Dow Jones Industrial Average Index (DJIA) and New York Stock Exchange. Our method provides an optimal multiple kernel and optimal parameters in Support Vector Regression, improving the accuracy of prediction. The proposed approach is structured on two levels. The macro-level uses a breeder genetic algorithm for choosing the optimal multiple kernel and the SVR optimal parameters. The fitness function of each chromosome is computed in the micro-level using a SVR algorithm. The regression model based on the optimal multiple kernel and optimal parameters is then validated and used for forecasting. The experimental results prove that OMK–SVR performs better than GEP, RBF-SVR and ELM for predicting the future behavior of the study series. A sensitivity study with respect to the number of kernels from the multiple kernel used by OMK–SVR and with respect to the ratio between training and testing data sets was conducted.