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

Erol Egrioglu

Economic Modelling, 2014, vol. 38, issue C, 633-639

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.

Keywords: Fuzzy time series; Particle swarm optimization; Fuzzy c-means; Forecasting; Define fuzzy relation (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:38:y:2014:i:c:p:633-639

DOI: 10.1016/j.econmod.2014.02.017

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