IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v312y2022i1d10.1007_s10479-021-04041-z.html
   My bibliography  Save this article

A forecasting model for time series based on improvements from fuzzy clustering problem

Author

Listed:
  • Tai Vovan

    (Can Tho University)

  • Luan Nguyenhuynh

    (Can Tho University)

  • Thuy Lethithu

    (Vinh Long University Technology and Education)

Abstract

This article proposes a new fuzzy time series model that can interpolate historical data, and forecast effectively for the future. It is combination of the improved steps from the existing models. There are problems to use the percentage variations of series between consecutive periods of time as a universal set, to divide the universal set into clusters by the automatic algorithm based on the similarity between elements, to determine the relationships between elements in the series and the divided clusters by the improved fuzzy cluster analysis algorithm, and to interpolate the historical data and to forecast for future by new principle. The proposed algorithm is performed quickly and efficiently by the established Matlab procedure. It is illustrated by an example, and tested for many other data sets, especially for 3003 series in M3-Competition data. Comparing to the existing models, the proposed model always gives the best result. We also apply the proposed model in forecasting the salty peak for a coastal province of Vietnam. Examples and application show the potential of the studied problem.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:312:y:2022:i:1:d:10.1007_s10479-021-04041-z
    DOI: 10.1007/s10479-021-04041-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-021-04041-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/s10479-021-04041-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. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. Chellai, Fatih, 2022. "Forecasting using Fuzzy Time Series," MPRA Paper 113848, University Library of Munich, Germany.
    7. 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.
    8. 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.
    9. Maigana Alhaji Bakawu, 2021. "Demographic Trends Forecasting: A Panacea for Sustainable Education Development Policies," International Journal of Science and Business, IJSAB International, vol. 5(7), pages 118-126.

    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:annopr:v:312:y:2022:i:1:d:10.1007_s10479-021-04041-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.