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An improved fuzzy time series forecasting model using variations of data

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  • Tai Vovan

    (Can Tho University)

Abstract

This study proposes an improved fuzzy time series (IFTS) forecasting model using variations of data that can interpolate historical data and forecast the future. The parameters in this model are chosen by algorithms to obtain the most suitable values for each data set. The calculation of the IFTS model can be performed conveniently and efficiently by a procedure within the R statistical software that has been stored in the AnalyseTS package. The proposed model is also used in the forecasting of two real problems in Vietnam: the penetration of salt and the total population. These numerical examples show the advantages of the proposed model in comparison with existing models and illustrate its effectiveness in practical applications.

Suggested Citation

  • Tai Vovan, 2019. "An improved fuzzy time series forecasting model using variations of data," Fuzzy Optimization and Decision Making, Springer, vol. 18(2), pages 151-173, June.
    • Handle: RePEc:spr:fuzodm:v:18:y:2019:i:2:d:10.1007_s10700-018-9290-7
    DOI: 10.1007/s10700-018-9290-7
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    References listed on IDEAS

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    1. Huarng, Kunhuang & Yu, Tiffany Hui-Kuang, 2006. "The application of neural networks to forecast fuzzy time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(2), pages 481-491.
    2. 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.
    3. Cagdas Hakan Aladag & Ufuk Yolcu & Erol Egrioglu & I. Burhan Turksen, 2016. "Type-1 fuzzy time series function method based on binary particle swarm optimisation," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 8(1), pages 2-13.
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    Cited by:

    1. Yulong Bai & Lihong Tang & Manhong Fan & Xiaoyan Ma & Yang Yang, 2020. "Fuzzy First-Order Transition-Rules-Trained Hybrid Forecasting System for Short-Term Wind Speed Forecasts," Energies, MDPI, vol. 13(13), pages 1-21, June.
    2. Cheng-Hong Yang & Jen-Chung Shao & Yen-Hsien Liu & Pey-Huah Jou & Yu-Da Lin, 2022. "Application of Fuzzy-Based Support Vector Regression to Forecast of International Airport Freight Volumes," Mathematics, MDPI, vol. 10(14), pages 1-18, July.
    3. 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.
    4. 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.
    5. Thi-Nham Le & Thanh-Tuan Dang, 2022. "An Integrated Approach for Evaluating the Efficiency of FDI Attractiveness: Evidence from Vietnamese Provincial Data from 2012 to 2022," Sustainability, MDPI, vol. 14(20), pages 1-25, October.
    6. Cheng-Hong Yang & Borcy Lee & Pey-Huah Jou & Yu-Fang Chung & Yu-Da Lin, 2023. "Analysis and Forecasting of International Airport Traffic Volume," Mathematics, MDPI, vol. 11(6), pages 1-19, March.

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