Variance Minimization Least Squares Support Vector Machines for Time Series Analysis

Abstract

Here we propose a novel machine learning method for time series forecasting which is based on the widely-used Least Squares Support Vector Machine (LS-SVM) approach. The objective function of our method contains a weighted variance minimization part as well. This modification makes the method more efficient in time series forecasting, as this paper will show. The proposed method is a generalization of the well-known LS-SVM algorithm. It has similar advantages like the applicability of the kernel-trick, it has a linear and unique solution, and a short computational time, but can perform better in certain scenarios. The main purpose of this paper is to introduce the novel Variance Minimization Least Squares Support Vector Machine (VMLS-SVM) method and to show its superiority through experimental results using standard benchmark time series prediction datasets.