IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/113848.html
   My bibliography  Save this paper

Forecasting using Fuzzy Time Series

Author

Listed:
  • Chellai, Fatih

Abstract

This chapter is a very short introduction to Fuzzy Time Series (FTS) models. The aim is to present an overview of the concepts of fuzzy logic, fuzzy set theory, and fuzzy time series framework. Accordingly, the chapter has a full application dimension of the FTS models as a main vocation. The R program was used to fit and forecast the principal FTS models, where real datasets of road traffic accidents in Algeria have been used. This chapter is organized as follows; the first section presents the concept of fuzzy logic, the second section is devoted to the Fuzzy Time Series, where we define a fuzzy set and universe of discourse. The third section summarizes the main models of fuzzy time series, precisely; we presented the (Song & Chissom, 1993) model, the (Chen, 1996) model, the Heuristic (Huarng, 2001) model, the (Abbasov & Mamedova, 2003) model, the (Chen & Hsu, 2004) model, and the (Singh, 2008) model. The fourth section is a case application of these models on the number of accidents in Algeria; the “AnalyzeTS” package of the R program was used to demonstrate the steps of estimation and forecasting.

Suggested Citation

  • Chellai, Fatih, 2022. "Forecasting using Fuzzy Time Series," MPRA Paper 113848, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:113848
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/113848/1/MPRA_paper_113848.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    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.
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    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. 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.
    7. 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.

    More about this item

    Keywords

    Fuzzy logic; Forecasting; Time Series;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:pra:mprapa:113848. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.