A novel choquet integral composition forecasting model based on m-density

  • Authors:

  • Hsiang-Chuan Liu;Shang-Ling Ou;Hsien-Chang Tsai;Yih-Chang Ou;Yen-Kuei Yu

  • Affiliations:

  • Department of Biomedical Informatics, Asia University, Taichung, Taiwan, R.O.C. and Graduate Institute of Educational Measurement and Statistics, National Taichung University of Education, Taichun ...;Department of Agronomy, National Chung Hsing University, Taichung, Taiwan, R.O.C.;Department of Biology, National Changhua University of Education, Changhua City, Taiwan, R.O.C.;Department of Finance, Ling Tung University, Taichung, Taiwan, R.O.C.;Graduate Institute of Educational Measurement and Statistics, National Taichung University of Education, Taichung, Taiwan, R.O.C.

  • Venue:
  • ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
  • Year:
  • 2012

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Abstract

In this paper, a novel density, M-density, was proposed. Based on this new density, a novel composition forecasting model was also proposed. For comparing the forecasting efficiency of this new density with the well-known density, N-density, a real data experiment was conducted. The performances of Choquet integral composition forecasting model with extensional L-measure, λ-measure and P-measure, by using M-density and N-density, respectively, a ridge regression composition forecasting model and a multiple linear regression composition forecasting model and the traditional linear weighted composition forecasting model were compared. Experimental result showed that the Choquet integral composition forecasting model with respect to extensional L-measure based on M-density outperforms other composition forecasting models. Furthermore, for each fuzzy measure, including the LE-measure, L-measure, λ-measure and P-measure, the M-density based Choquet integral composition forecasting model is better than the N-density based.