Wavelet support vector machines and multi-elitist particle swarm optimization for time series forecasting

Jerzy Martyna


In this paper, we present a new method for time series forecasting based on wavelet support vector machines (WSVM). To better represent any curve in L^2(R^n) space (quadratic continuous integral space), we used a new kernel function. This function is the wavelet function. The SVM with wavelet kernel function is referred to as a wavelet SVM. In order to determine the optimal parameter of the WSVM, the multi-elitist particle swarm optimization (PSO) was used. Computational results demonstrate the effectiveness of the proposed method over the traditional methods.


wavelet support vector machine; multielitist particle swarm optimization; time series forecasting

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DOI: http://dx.doi.org/10.21936/si2011_v32.n2A.277