A generalized method for forecasting based on fuzzy time series

  • Authors:

  • Wangren Qiu;Xiaodong Liu;Hailin Li

  • Affiliations:

  • Research Center of Information and Control, Dalian University of Technology, Dalian 116024, China and Department of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen 333001, China;Research Center of Information and Control, Dalian University of Technology, Dalian 116024, China;Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

Song and Chissom proposed fuzzy time series and many researchers have made much effort to improve it. Ensemble technique is an effective method of improving the classification accuracy in data mining area. This study applies ensemble technique to fuzzy time series to propose a new model, and prove that Song's and Chissom 1993a, 1993b, Chen (1996) and Lee et al. (2009) models can be approximated by the proposed model via the limitation of the fuzzy weights. The impact on the performance of the proposal model is discussed. Both university enrollment and Shanghai stock index are chosen as the forecasting targets. The empirical results not only testify the above assertion, but also show that the proposed model can provide better overall forecasting results than the previous models with appropriate parameters.