IDEAS home Printed from https://ideas.repec.org/a/spr/fuzodm/v18y2019i3d10.1007_s10700-018-9298-z.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10700-018-9298-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10700-018-9298-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Jue Lu & Lianlian Zhou & Wenxing Zeng & Anshui Li, 2024. "Music statistics: uncertain logistic regression models with applications in analyzing music," Fuzzy Optimization and Decision Making, Springer, vol. 23(4), pages 637-654, December.
    6. Liu, Z. & Yang, Y., 2021. "Pharmacokinetic model based on multifactor uncertain differential equation," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    7. 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.
    8. 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.
    9. 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.
    10. Xiaoxia Huang & Xue Meng & Xiaozhu Xu, 2024. "Portfolio selection with second order uncertain dominance constraint," Fuzzy Optimization and Decision Making, Springer, vol. 23(4), pages 561-575, December.
    11. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pourmahmoud, Jafar & Bagheri, Narges, 2023. "Uncertain Malmquist productivity index: An application to evaluate healthcare systems during COVID-19 pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    2. Zhe Liu & Ying Yang, 2020. "Least absolute deviations estimation for uncertain regression with imprecise observations," Fuzzy Optimization and Decision Making, Springer, vol. 19(1), pages 33-52, March.
    3. Bao Jiang & Enxin Chi & Jian Li, 2022. "Uncertain Data Envelopment Analysis for Cross Efficiency Evaluation with Imprecise Data," Mathematics, MDPI, vol. 10(13), pages 1-9, June.
    4. Jianhua Ding & Zhiqiang Zhang, 2021. "Statistical inference on uncertain nonparametric regression model," Fuzzy Optimization and Decision Making, Springer, vol. 20(4), pages 451-469, December.
    5. Xinxin Wang & Zeshui Xu & Yong Qin, 2022. "Structure, trend and prospect of operational research: a scientific analysis for publications from 1952 to 2020 included in Web of Science database," Fuzzy Optimization and Decision Making, Springer, vol. 21(4), pages 649-672, December.
    6. Xing Zhao & Xin Zhang, 2022. "Research on the Evaluation and Regional Differences in Carbon Emissions Efficiency of Cultural and Related Manufacturing Industries in China’s Yangtze River Basin," Sustainability, MDPI, vol. 14(17), pages 1-22, August.
    7. Enxin Chi & Bao Jiang & Luyao Peng & Yu Zhong, 2022. "Uncertain Network DEA Models with Imprecise Data for Sustainable Efficiency Evaluation of Decentralized Marine Supply Chain," Energies, MDPI, vol. 15(15), pages 1-16, July.
    8. Bao Jiang & Wenxue Feng & Jian Li, 2022. "Uncertain random data envelopment analysis for technical efficiency," Fuzzy Optimization and Decision Making, Springer, vol. 21(1), pages 1-20, March.
    9. Utsav Pandey & Sanjeet Singh, 2022. "Data envelopment analysis in hierarchical category structure with fuzzy boundaries," Annals of Operations Research, Springer, vol. 315(2), pages 1517-1549, August.
    10. Mohammad Jamshidi & Masoud Sanei & Ali Mahmoodirad & Farhad Hoseinzadeh Lotfi & Ghasem Tohidi, 2021. "Uncertain SBM data envelopment analysis model: A case study in Iranian banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2674-2689, April.
    11. Jie Wu & Lulu Shen & Ganggang Zhang & Zhixiang Zhou & Qingyuan Zhu, 2024. "Efficiency evaluation with data uncertainty," Annals of Operations Research, Springer, vol. 339(3), pages 1379-1403, August.
    12. Goker, Nazli & Karsak, E.Ertugrul, 2021. "Two-stage common weight DEA-Based approach for performance evaluation with imprecise data," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:fuzodm:v:18:y:2019:i:3:d:10.1007_s10700-018-9298-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.