A novel fuzzy time series forecasting method based on the improved artificial fish swarm optimization algorithm
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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.
KeywordsFuzzy time series Forecasting Artificial fish swarm algorithm Levy flight HAFSA
The authors express their gratitude to the editor and the anonymous reviewers for their insightful and constructive comments. This work was supported by the Graduate Teaching Reform Research Program of Chongqing Municipal Education Commission (No. YJG143010), Chongqing research and innovation Project of Graduate students (No. CYS16172), and the National Natural Science Foundation of China (Nos. 61472056, 11671001).
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Conflict of interest
The authors declare that they have no conflict of interest.
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