IDEAS home Printed from https://ideas.repec.org/a/ibn/masjnl/v4y2010i5p162.html
   My bibliography  Save this article

Application of Markov Chains to Analyze and Predict the Time Series

Author

Listed:
  • Tie Liu

Abstract

Markov chains are usually used in modeling many practical problems. They are also effective in modeling time series. In this paper, we apply the Markov chains model to analyze and predict the time series. Some series can be expressed by a first-order discrete-time Markov chain and others must be expressed by a higher-order Markov chain model. Numerical examples are given. The results show that the performance and effectiveness of the Markov chain model to predict the time series is very well.

Suggested Citation

  • Tie Liu, 2010. "Application of Markov Chains to Analyze and Predict the Time Series," Modern Applied Science, Canadian Center of Science and Education, vol. 4(5), pages 162-162, May.
    • Handle: RePEc:ibn:masjnl:v:4:y:2010:i:5:p:162
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/mas/article/download/6040/4874
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/mas/article/view/6040
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wai Ki Ching & Eric S. Fung & Michael K. Ng, 2004. "Higher‐order Markov chain models for categorical data sequences," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(4), pages 557-574, June.
    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. Yang, Ningkang & Han, Lijin & Xiang, Changle & Liu, Hui & Li, Xunmin, 2021. "An indirect reinforcement learning based real-time energy management strategy via high-order Markov Chain model for a hybrid electric vehicle," Energy, Elsevier, vol. 236(C).
    2. Topuz, Kazim & Urban, Timothy L. & Yildirim, Mehmet B., 2024. "A Markovian score model for evaluating provider performance for continuity of care—An explainable analytics approach," European Journal of Operational Research, Elsevier, vol. 317(2), pages 341-351.
    3. Suryadeepto Nag & Sankarshan Basu & Siddhartha P. Chakrabarty, 2022. "Modeling the Commodity Prices of Base Metals in Indian Commodity Market Using a Higher Order Markovian Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 159-171, March.
    4. Flavio Ivo Riedlinger & João Nicolau, 2020. "The Profitability in the FTSE 100 Index: A New Markov Chain Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(1), pages 61-81, March.
    5. Anton E. Kulagin & Alexander V. Shapovalov, 2023. "Analytical Description of the Diffusion in a Cellular Automaton with the Margolus Neighbourhood in Terms of the Two-Dimensional Markov Chain," Mathematics, MDPI, vol. 11(3), pages 1-18, January.
    6. Chenfeng Xiong & Di Yang & Lei Zhang, 2018. "A High-Order Hidden Markov Model and Its Applications for Dynamic Car Ownership Analysis," Service Science, INFORMS, vol. 52(6), pages 1365-1375, December.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    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:ibn:masjnl:v:4:y:2010:i:5:p:162. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.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.