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A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series

Author

Listed:
  • Abhishekh

    (Department of Mathematics, Institute of Science, Banaras Hindu University, Varanasi, India)

  • Surendra Singh Gautam

    (#x2020;Government Polytechnic College, Gariyaband, Chhattisgarh, India)

  • S. R. Singh

    (Department of Mathematics, Institute of Science, Banaras Hindu University, Varanasi, India)

Abstract

Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In this paper, we propose an enhanced and versatile method of forecasting using the concept of intuitionistic fuzzy time series (FTS) based on their score function. The developed method has been presented in the form of simple computational steps of forecasting instead of complicated max–min compositions operator of intuitionistic fuzzy sets to compute the relational matrix R. Also, the proposed method is based on the maximum score and minimum accuracy function of intuitionistic fuzzy numbers (IFNs) to fuzzify the historical time series data. Further intuitionistic fuzzy logical relationship groups are defined and also provide a forecasted value and lies in an interval and is more appropriate rather than a crisp value. Furthermore, the proposed method has been implemented on the historical student enrollments data of University of Alabama and obtains the forecasted values which have been compared with the existing methods to show its superiority. The suitability of the proposed model has also been examined to forecast the movement of share market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE). The results of the comparison of MSE and MAPE indicate that the proposed method produces more accurate forecasting results.

Suggested Citation

  • Abhishekh & Surendra Singh Gautam & S. R. Singh, 2018. "A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 91-111, March.
    • Handle: RePEc:wsi:nmncxx:v:14:y:2018:i:01:n:s1793005718500072
    DOI: 10.1142/S1793005718500072
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    Citations

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    Cited by:

    1. Eren Bas & Erol Egrioglu & Taner Tunc, 2023. "Multivariate Picture Fuzzy Time Series: New Definitions and a New Forecasting Method Based on Pi-Sigma Artificial Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 139-164, January.
    2. Surendra Singh Gautam & Abhishekh & S. R. Singh, 2020. "A modified weighted method of time series forecasting in intuitionistic fuzzy environment," OPSEARCH, Springer;Operational Research Society of India, vol. 57(3), pages 1022-1041, September.
    3. Gholamreza Hesamian & Arne Johannssen & Nataliya Chukhrova, 2023. "A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data," Mathematics, MDPI, vol. 11(13), pages 1-17, June.
    4. Abhishekh & A. K. Nishad, 2019. "A Novel Ranking Approach to Solving Fully LR-Intuitionistic Fuzzy Transportation Problems," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 95-112, March.

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