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Type-1 fuzzy time series function method based on binary particle swarm optimisation

Author

Listed:
  • Cagdas Hakan Aladag
  • Ufuk Yolcu
  • Erol Egrioglu
  • I. Burhan Turksen

Abstract

For time series forecasting four kinds of fuzzy-based approaches can be used. These are fuzzy regression techniques, fuzzy time series methods, fuzzy inference systems, and fuzzy function approaches. There are some major problems in using fuzzy regression techniques and fuzzy inference systems for time series forecasting. Therefore, it would be wise to use a forecasting approach which combines fuzzy time series and fuzzy function approaches. In this study, a fuzzy time series forecasting method based on fuzzy function approach is proposed by adopting fuzzy function approach to time series forecasting. And, the proposed approach is called type-1 fuzzy time series function approach. Also, in the proposed approach, the lagged variables of the system are determined by using binary particle swarm optimisation. In order to evaluate the performance of the proposed method, it has been applied to well-known time series of and Istanbul stock exchange dataset.

Suggested Citation

  • Cagdas Hakan Aladag & Ufuk Yolcu & Erol Egrioglu & I. Burhan Turksen, 2016. "Type-1 fuzzy time series function method based on binary particle swarm optimisation," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 8(1), pages 2-13.
    • Handle: RePEc:ids:injdan:v:8:y:2016:i:1:p:2-13
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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    Cited by:

    1. Tai Vovan, 2019. "An improved fuzzy time series forecasting model using variations of data," Fuzzy Optimization and Decision Making, Springer, vol. 18(2), pages 151-173, June.

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