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PSO-based high order time invariant fuzzy time series method: Application to stock exchange data

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  • Egrioglu, Erol

Abstract

Fuzzy time series methods are effective techniques to forecast time series. Fuzzy time series methods are based on fuzzy set theory. In the early years, classical fuzzy set operations were used in the fuzzy time series methods. In recent years, artificial intelligence techniques have been used in different stages of fuzzy time series methods. In this paper, a novel fuzzy time series method which is based on particle swarm optimization is proposed. A high order fuzzy time series forecasting model is used in the proposed method. In the proposed method, determination of fuzzy relations is performed by estimating the optimal fuzzy relation matrix. The performance of the proposed method is compared to some methods in the literature by using three real world time series. It is shown that the proposed method has better performance than other methods in the literature.

Suggested Citation

  • Egrioglu, Erol, 2014. "PSO-based high order time invariant fuzzy time series method: Application to stock exchange data," Economic Modelling, Elsevier, vol. 38(C), pages 633-639.
    • Handle: RePEc:eee:ecmode:v:38:y:2014:i:c:p:633-639
    DOI: 10.1016/j.econmod.2014.02.017
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    References listed on IDEAS

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    1. Cheng, Ching-Hsue & Wei, Liang-Ying & Liu, Jing-Wei & Chen, Tai-Liang, 2013. "OWA-based ANFIS model for TAIEX forecasting," Economic Modelling, Elsevier, vol. 30(C), pages 442-448.
    2. Cheng, Ching-Hsue & Wei, Liang-Ying, 2014. "A novel time-series model based on empirical mode decomposition for forecasting TAIEX," Economic Modelling, Elsevier, vol. 36(C), pages 136-141.
    3. Wei, Liang-Ying, 2013. "A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX," Economic Modelling, Elsevier, vol. 33(C), pages 893-899.
    4. Jilani, Tahseen Ahmed & Burney, Syed Muhammad Aqil, 2008. "A refined fuzzy time series model for stock market forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2857-2862.
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    Cited by:

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    2. Marek Bundzel & Tomas Kasanicky & Richard Pincak, 2016. "Using String Invariants for Prediction Searching for Optimal Parameters," Papers 1606.06003, arXiv.org.
    3. Fernando G. Bernardes & Douglas A. G. Vieira & Vasile Palade & Rodney R. Saldanha, 2018. "Winds of Change: How Up-To-Date Forecasting Methods Could Help Change Brazilian Wind Energy Policy and Save Billions of US$," Energies, MDPI, vol. 11(11), pages 1-22, October.

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