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Fuzzy Forecast Based on Fuzzy Time Series

In: Time Series Analysis - Data, Methods, and Applications

Author

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  • Ming-Tao Chou

Abstract

This chapter mainly uses fuzzy time series for interval prediction and long-term significance level analysis. In this study, the Taiwan Shipping and Transportation Index (Taiwan STI) is used to illustrate the prediction process. Nine steps have been used to establish the interval prediction of the Taiwan Shipping and Transportation Index (Taiwan STI), and ?S is called a long-term significance level (up/down/stable) is used to illustrate the long-term prediction significance level. By means of interval prediction and long-term prediction significance level, the future trends for this index and more internal messages related to this index can be provided to relevant researchers.

Suggested Citation

  • Ming-Tao Chou, 2019. "Fuzzy Forecast Based on Fuzzy Time Series," Chapters, in: Chun-Kit Ngan (ed.), Time Series Analysis - Data, Methods, and Applications, IntechOpen.
    • Handle: RePEc:ito:pchaps:165840
    DOI: 10.5772/intechopen.82843
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    File URL: https://www.intechopen.com/chapters/65010
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    Cited by:

    1. Ming-Tao Chou, 2016. "Fuzzy Time Series Theory Application for the China Containerized Freight Index," Applied Economics and Finance, Redfame publishing, vol. 3(3), pages 127-135, August.

    More about this item

    Keywords

    fuzzy time series; interval prediction; long-term prediction significance level;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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