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Soft Computing

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19.04.2017 | Methodologies and Application

A novel fuzzy time series forecasting method based on the improved artificial fish swarm optimization algorithm

verfasst von: Sidong Xian, Jianfeng Zhang, Yue Xiao, Jia Pang

Erschienen in: Soft Computing | Ausgabe 12/2018

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Abstract

Recently, many forecasting methods have been proposed for the analysis of fuzzy time series. The main factors that affect the results of the forecasting of these models are partition universe of discourse and determination of fuzzy relations. In this paper, a novel fuzzy time series forecasting method which uses a hybrid artificial fish swarm optimization algorithm for the determination of interval lengths is proposed. Firstly, we introduce the chemotactic behavior of Bacterial foraging optimization into foraging behavior. Secondly, the Levy flight is used as the mutation operator for a mutation strategy. Finally, the new proposed method is applied to a fuzzy time series forecasting and the experimental results show that the proposed model obtain better forecasting results than those of other existing models. It proves the feasibility and validity of above-mentioned approaches.

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Titel: A novel fuzzy time series forecasting method based on the improved artificial fish swarm optimization algorithm
verfasst von: Sidong Xian
Jianfeng Zhang
Yue Xiao
Jia Pang
Publikationsdatum: 19.04.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 12/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-2601-z

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