A hybrid wavelet analysis and support vector machines in forecasting development of manufacturing

Authors:
Xuesong Guo;Linyan Sun;Gang Li;Song Wang
Affiliations:
School of Management, Xi'an Jiaotong University, 710049, P.R. China and The State Key Lab for Manufacturing Systems Engineering, Xi'an, 710049, P.R. China;School of Management, Xi'an Jiaotong University, 710049, P.R. China and The State Key Lab for Manufacturing Systems Engineering, Xi'an, 710049, P.R. China;School of Management, Xi'an Jiaotong University, 710049, P.R. China and The State Key Lab for Manufacturing Systems Engineering, Xi'an, 710049, P.R. China;School of Management, Xi'an Jiaotong University, 710049, P.R. China and The State Key Lab for Manufacturing Systems Engineering, Xi'an, 710049, P.R. China
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

This paper proposes a hybrid methodology that exploits strengths of wavelet analysis and support vector machine model in forecasting time series, and deals with the application of proposed methodology in manufacturing time series forecasting. This method is characteristic of the preprocessing of sample data using wavelet transformation for forecast, i.e., the data sequence of evolvement of share of some sectors in manufacturing is first mapped into several time-frequency domains, and then a support vector machine is established for each domain. The final forecasting results are the algebraic sums of all the forecasted components obtained by respective support vector machine models corresponding to different time-frequency domains. Nevertheless, one of disadvantages of the method is dilemma of selection of values of parameters in support vector machine because the way of selecting values for the parameters will affect the generalization performance remarkably. In this paper, chaos optimization is applied to accomplish selection of values of parameters. Results of experiments based on gross values of textile product in Japan suggest that this hybrid method can both achieve higher accuracy in manufacturing forecasting.