IDEAS home Printed from https://ideas.repec.org/a/asi/joasrj/v7y2017i5p196-205id3816.html
   My bibliography  Save this article

Hidden Markov Model for Time Series Prediction

Author

Listed:
  • Muhammad Hanif
  • Faiza Sami
  • Mehvish Hyder
  • Muhammad Iqbal Ch

Abstract

The Hidden Markov Model (HMM) is a powerful statistical tool for modeling generative sequences that can be characterized by an underlying process generating an observable sequence. Hidden Markov Model is one of the most basic and extensively used statistical tools for modeling the discrete time series. In this paper using transition probabilities and emission probabilities different algorithm are computed and modeled the series and the algorithms to solve the problems related to the hidden markov model are presented. Hidden markov models face some problems like learning about the model, evaluation process and estimate of parameters included in the model. The solution to these problems as forward-backward, Viterbi, and Baum Welch algorithm are discussed respectively and also useful for computation. A new hidden markov model is developed and estimates its parameters and also discussed the state space model.

Suggested Citation

  • Muhammad Hanif & Faiza Sami & Mehvish Hyder & Muhammad Iqbal Ch, 2017. "Hidden Markov Model for Time Series Prediction," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 7(5), pages 196-205.
    • Handle: RePEc:asi:joasrj:v:7:y:2017:i:5:p:196-205:id:3816
    as

    Download full text from publisher

    File URL: https://archive.aessweb.com/index.php/5003/article/view/3816/6009
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Mourad Mroua & Ahlem Lamine, 2023. "Financial time series prediction under Covid-19 pandemic crisis with Long Short-Term Memory (LSTM) network," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.

    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:asi:joasrj:v:7:y:2017:i:5:p:196-205:id:3816. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Robert Allen (email available below). General contact details of provider: https://archive.aessweb.com/index.php/5003/ .

    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.