The impact of preprocessing on support vector regression and neural networks in time series prediction

Abstract

Support Vector Regression (SVR) and Neural Networks (NN) have been successfully applied to forecasting and time series prediction. While conventional statistical methods require specific data preprocessing prior to the forecasting step both, SVR as well as NN need less efforts for the respective tasks due to their theoretical properties. On the other hand, it is known that preprocessing affects performance of classifiers built using these methods. In this paper we analyze how preprocessing affects the forecasting performance using SVR and NN and provide detailed insights applying several preprocessing strategies to different artificial time series. There is evidence to prefer linear scaling into the interval [-0.5,0.5] among the analyzed strategies. Future work is proposed in order to validate our findings and extend the experiments to alternative preprocessing strategies.

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