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

Egrioglu, Erol (2014) PSO-based high order time invariant fuzzy time series method:Application to stock exchange data. Economic Modelling, 38. pp. 633-639. ISSN 0264-9993

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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.

Item Type:
Journal Article
Journal or Publication Title:
Economic Modelling
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2000/2002
Subjects:
ID Code:
139539
Deposited By:
Deposited On:
17 Dec 2019 09:45
Refereed?:
Yes
Published?:
Published
Last Modified:
09 Sep 2020 06:35