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The forecasting model based on wavelet v-support vector machine
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DOI：10.1016/j.eswa.2008.09.048.png)
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Abstract
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Aiming at the series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the L-2(R-n) space (quadratic continuous integral space). A new wavelet support vector machine (WN v-SVM) is proposed based on wavelet theory and modified support vector machine. A particle swarm optimization (PSO) algorithm is designed to select the best parameters of WN v-SVM model in the scope of constraint permission. The results of application in car sale series forecasting show that the forecasting approach based on the PSOWN v-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than PSOW v-SVM and other traditional methods. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords:
Wavelet kernel
Support vector machine (SVM)
Particle swarm optimization
Sales forecasting
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