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A new fuzzy time series forecasting model based on clustering technique and normal fuzzy function

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Abstract

Time series forecasting is of great interest to managers and scientists because of the numerous benefits it offers. This study proposes three main improvements for forecasting to time series. First, we establish the percentage variation series between two consecutive times and use an automatic algorithm to divide it into clusters with a suitable number. This algorithm also determines the specific elements in each cluster. Second, a new fuzzy function with a normal type is built for each cluster. Finally, we develop the forecasting rule based on the previous two improvements. By combining these enhancements, we obtain an effective model for forecasting. The proposed model is presented step-by-step and executed rapidly using the MATLAB procedure. Compared to many models tested on the M3-Competition set with 3003 series and the M4-Competition set with 100,000 series, the proposed model obtains outstanding results. It also achieves competitive results when compared to existing models across several benchmarks and real series.

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Acknowledgements

This research is funded by Ministry of Education and Training in Vietnam under grant number B2023-TCT-06.

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Author notes

  1. L. Nguyen-Huynh, T. Vo-Van have contributed equally to this work.

Authors and Affiliations

  1. Faculty of Mechanical–Electrical and Computer Engineering, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam

    Luan Nguyen-Huynh

  2. College of Natural Science, Can Tho University, Can Tho City, Vietnam

    Tai Vo-Van

Authors
  1. Luan Nguyen-Huynh
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  2. Tai Vo-Van
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Contributions

Luan Nguyen-Huynh: Establish for the Matlab procedure for the proposed model, and perform the numerical examples and applications Tai Vo-Van: build the proposed model and write the manuscript.

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Correspondence to Tai Vo-Van.

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Nguyen-Huynh, L., Vo-Van, T. A new fuzzy time series forecasting model based on clustering technique and normal fuzzy function. Knowl Inf Syst 65, 3489–3509 (2023). https://doi.org/10.1007/s10115-023-01875-w

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