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A computational method of forecasting based on fuzzy time series

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  • Singh, S.R.

Abstract

In this paper, a computational method of forecasting based on fuzzy time series have been developed to provide improved forecasting results to cope up the situation containing higher uncertainty due to large fluctuations in consecutive year's values in the time series data and having no visualization of trend or periodicity. The proposed model is of order three and uses a time variant difference parameter on current state to forecast the next state. The developed model has been tested on the historical student enrollments, University of Alabama to have comparison with the existing methods and has been implemented for forecasting of a crop production system of lahi crop, containing higher uncertainty. The suitability of the developed model has been examined in comparison with the other models to show its superiority.

Suggested Citation

  • Singh, S.R., 2008. "A computational method of forecasting based on fuzzy time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(3), pages 539-554.
    • Handle: RePEc:eee:matcom:v:79:y:2008:i:3:p:539-554
    DOI: 10.1016/j.matcom.2008.02.026
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    References listed on IDEAS

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    1. Yu, Hui-Kuang, 2005. "Weighted fuzzy time series models for TAIEX forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 609-624.
    2. Yu, Hui-Kuang, 2005. "A refined fuzzy time-series model for forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(3), pages 657-681.
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    Cited by:

    1. Tai Vovan & Luan Nguyenhuynh & Thuy Lethithu, 2022. "A forecasting model for time series based on improvements from fuzzy clustering problem," Annals of Operations Research, Springer, vol. 312(1), pages 473-493, May.
    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. Fatih Chellai, 2022. "Forecasting Models Based on Fuzzy Logic: An Application on International Coffee Prices," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 26(4), pages 1-16, December.
    4. Aljarallah, Ruba A., 2021. "An assessment of the economic impact of natural resource rents in kingdom of Saudi Arabia," Resources Policy, Elsevier, vol. 72(C).
    5. Himadri Ghosh & S. Chowdhury & Prajneshu, 2016. "An improved fuzzy time-series method of forecasting based on L -- R fuzzy sets and its application," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(6), pages 1128-1139, May.
    6. Chellai, Fatih, 2022. "Forecasting using Fuzzy Time Series," MPRA Paper 113848, University Library of Munich, Germany.
    7. Bogdan Oancea & Richard Pospíšil & Marius Nicolae Jula & Cosmin-Ionuț Imbrișcă, 2021. "Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods," Mathematics, MDPI, vol. 9(19), pages 1-17, October.
    8. Wulan Anggraeni & Sudradjat Supian & Sukono & Nurfadhlina Abdul Halim, 2023. "Single Earthquake Bond Pricing Framework with Double Trigger Parameters Based on Multi Regional Seismic Information," Mathematics, MDPI, vol. 11(3), pages 1-44, January.

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