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High Order Time Series Forecasting using Fuzzy Discretization

Author

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  • Mahua Bose

    (Department of Computer Science & Engineering, University of Kalyani, Nadia, India)

  • Kalyani Mali

    (Department of Computer Science & Engineering, University of Kalyani, Nadia, India)

Abstract

In recent years, various methods for forecasting fuzzy time series have been presented in different areas, such as stock price, enrollments, weather, production etc. It is observed that in most of the cases, static length of intervals/equal length of interval has been used. Length of the interval has significant role on forecasting accuracy. The objective of this present study is to incorporate the idea of fuzzy discretization into interval creation and examine the effect of positional information of elements within a group or interval to the forecast. This idea outperforms the existing high order forecast methods using fixed interval. Experiments are carried on three datasets including Lahi production data, enrollment data and rainfall data which deal with a lot of uncertainty.

Suggested Citation

  • Mahua Bose & Kalyani Mali, 2016. "High Order Time Series Forecasting using Fuzzy Discretization," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 5(4), pages 147-164, October.
    • Handle: RePEc:igg:jfsa00:v:5:y:2016:i:4:p:147-164
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