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On Optimal Forecasting with Soft Computation for Nonlinear Time Series

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Abstract

In this paper we present a new approach on optimal forecasting by using the fuzzy set theory and soft computing methods for the dynamic data analysis. This research is based on the concepts of fuzzy membership function as well as the natural selection of evolution theory. Some discussions in the sensitivity of the design of fuzzy processing will be provided. Through the design of genetic evolution, the AIC criteria is used as the adjust function, and the fuzzy memberships function of each gene model are calculated. Simulation and empirical examples show that our proposed forecasting technique can give an optimal forecasting in time series analysis.

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Authors and Affiliations

  1. Department of Business Administration, ChengShiu Institute of Business & Technology, Kaohsiung 833, Kaohsiuang, Taiwan

    Shuenn-Ren Chen

  2. Department of Mathematical Sciences, National Chengchi University, Wen-shan 116, Taipei, Taiwan

    Berlin Wu

Authors
  1. Shuenn-Ren Chen
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  2. Berlin Wu
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Chen, SR., Wu, B. On Optimal Forecasting with Soft Computation for Nonlinear Time Series. Fuzzy Optimization and Decision Making 2, 215–228 (2003). https://doi.org/10.1023/A:1025090420345

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  • DOI: https://doi.org/10.1023/A:1025090420345

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