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Uncertain time series analysis with imprecise observations

Author

Listed:
  • Xiangfeng Yang

    (University of International Business and Economics)

  • Baoding Liu

    (Tsinghua University)

Abstract

Time series analysis is a method to predict future values based on previously observed values. Assuming the observed values are imprecise and described by uncertain variables, this paper proposes an approach of uncertain time series. By employing the principle of least squares, a minimization problem is derived to calculate the unknown parameters in the uncertain time series model. In addition, residual and confidence interval are also proposed. Finally, some numerical examples are given.

Suggested Citation

  • Xiangfeng Yang & Baoding Liu, 2019. "Uncertain time series analysis with imprecise observations," Fuzzy Optimization and Decision Making, Springer, vol. 18(3), pages 263-278, September.
    • Handle: RePEc:spr:fuzodm:v:18:y:2019:i:3:d:10.1007_s10700-018-9298-z
    DOI: 10.1007/s10700-018-9298-z
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    References listed on IDEAS

    as
    1. Zahra Mohmmad Nejad & Alireza Ghaffari-Hadigheh, 2018. "A novel DEA model based on uncertainty theory," Annals of Operations Research, Springer, vol. 264(1), pages 367-389, May.
    2. Waichon Lio & Baoding Liu, 2018. "Uncertain data envelopment analysis with imprecisely observed inputs and outputs," Fuzzy Optimization and Decision Making, Springer, vol. 17(3), pages 357-373, September.
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    Citations

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    Cited by:

    1. Tingqing Ye & Baoding Liu, 2022. "Uncertain hypothesis test with application to uncertain regression analysis," Fuzzy Optimization and Decision Making, Springer, vol. 21(2), pages 157-174, June.
    2. Zhongfeng Qin & Qiqi Li, 2023. "An uncertain support vector machine with imprecise observations," Fuzzy Optimization and Decision Making, Springer, vol. 22(4), pages 611-629, December.
    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. Tingqing Ye & Xiangfeng Yang, 2021. "Analysis and prediction of confirmed COVID-19 cases in China with uncertain time series," Fuzzy Optimization and Decision Making, Springer, vol. 20(2), pages 209-228, June.
    5. Liu, Z. & Yang, Y., 2021. "Pharmacokinetic model based on multifactor uncertain differential equation," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    6. Waichon Lio & Rui Kang, 2023. "Bayesian rule in the framework of uncertainty theory," Fuzzy Optimization and Decision Making, Springer, vol. 22(3), pages 337-358, September.
    7. Jinran Chen, 2024. "Prediction of global trade network evolution with uncertain multi-step time series forecasting method," Fuzzy Optimization and Decision Making, Springer, vol. 23(3), pages 387-414, September.
    8. Tingqing Ye & Baoding Liu, 2023. "Uncertain hypothesis test for uncertain differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 22(2), pages 195-211, June.
    9. Zhe Liu, 2021. "Uncertain growth model for the cumulative number of COVID-19 infections in China," Fuzzy Optimization and Decision Making, Springer, vol. 20(2), pages 229-242, June.

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