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HFS-based computational method for weighted fuzzy time series forecasting model using techniques of adaptive radius clustering and grey wolf optimization

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Abstract

Recently, hesitant fuzzy sets (HFSs) have been used extensively in time series forecasting. HFSs have inherent characteristics of addressing problem of non-stochastic hesitancy that is developed as a result of the availability of numerous techniques for fuzzification of time series data. In the present study, we have developed an HFS-based computational method for weighted fuzzy time series (WFTS) forecasting. The proposed method addresses the three main issues of appropriate partitioning of the universe of discourse (UOD) into unequal-length intervals, inclusion of hesitancy during fuzzification of time series data, recurrence, and weighting of fuzzy logical relation (FLR) in fuzzy time series forecasting. The proposed method uses a non-parametric clustering approach of adaptive radius clustering for accurate partitioning of UOD and HFS for inclusion of hesitancy in time series during process of fuzzification. The recurrence and weighting of FLRs are handled using Markov weights, which are then subsequently optimized by utilizing the popular swarm intelligence technique of grey wolf optimization. A simple computational method is provided that incorporates the optimized weights, thus simplifying the forecasting process. The proposed WFTS forecasting method is implemented in the Python programming language to forecast benchmark time series data of the University of Alabama and financial time series data of Taiwan stock exchange (TAIEX), market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE), India. The model's performance is measured by means of root-mean-square error (RMSE), and its reduced amount demonstrates the model's outperformance in forecasting of three diversified time series data taken in the study.

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Acknowledgements

First author thankfully acknowledges the Government of India's UGC (F. No. 16-9 (June 2018)/2019 (NET/CSIR)) assistance for this study. Authors acknowledge anonymous referees’ constructive comments that led to an improved version of the research paper. Authors are also very thankful to Dr. Prabha Pant, Associate Professor (English) from the Department of Humanities & Social Sciences, G. B. Pant University of Ag. & Technology, Pantnagar for pointing the grammatical and punctuation errors in the research paper.

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Council of Scientific and Industrial Research, India, UGC (F. No. 16-9 (June 2018)/2019 (NET/CSIR)).

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Authors and Affiliations

  1. Department of Mathematics, Statistics and Computer Science, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India, 263145

    Shivani Pant & Sanjay Kumar

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  1. Shivani Pant
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  2. Sanjay Kumar
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Shivani Pant: Conceptualization, Methodology, Simulation, Writing- Original draft preparation. Sanjay Kumar: Conceptualization, Methodology, Supervision, Writing- Reviewing and Editing.

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Correspondence to Sanjay Kumar.

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Pant, S., Kumar, S. HFS-based computational method for weighted fuzzy time series forecasting model using techniques of adaptive radius clustering and grey wolf optimization. Granul. Comput. 9, 11 (2024). https://doi.org/10.1007/s41066-023-00434-6

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