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Current IssueEditorial BoardOnline First ArticlesArchive

Research Article

volume 34 issue 3-4 (september-december 2019) : 225-228,   Doi: 10.18805/BKAP176

Application of Bayesian Inference in Time Series Analysis

K
Krishna Pada Sarkar
K
K. N. Singh
A
Achal Lama
M
Murari Kumar
B
Bishal Gurung
R
Rajeev Kumar
V
Vinaykumar L. N.
1<div style="text-align: justify;">ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi-110 012</div>

  • Submitted13-01-2020|

  • Accepted27-12-2019|

  • First Online 27-03-2020|

  • doi 10.18805/BKAP176

Cite article:- Sarkar Pada Krishna, Singh N. K., Lama Achal, Kumar Murari, Gurung Bishal, Kumar Rajeev, N. L. Vinaykumar (2020). Application of Bayesian Inference in Time Series Analysis. Bhartiya Krishi Anusandhan Patrika. 34(3): 225-228. doi: 10.18805/BKAP176.

ABSTRACT

Nowadays, the availability of huge computing facilities makes it easy to adopt a complex algorithm for various problem-solving. As a result, the Bayesian method of parameter estimation, which is based on Bayes’ theorem given by Thomas Bayes, gains its popularity in recent times. Time series modeling and forecasting is an important aspect of modeling. Model performance and forecasting accuracy can be improved largely using a Bayesian approach. In this article, some basic knowledge about Bayesian inference in time series has been discussed.

REFERENCES

  1. Andrieu, C., De Freitas, N., Doucet, A., and Jordan, M. I. (2003). An introduction to MCMC for machine learning, Machine learning, 50(1-2), 5-43.
  2. Box, G. E. P., Jenkins, G. M. and Reinsel G. C. (2007). Time-Series Analysis:  and Control, 4th edition. Willey Publication.
  3. Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, 2nd Edition. Springer, New York.
  4. Congdon, P. (2007). Bayesian statistical modelling. 2nd edition, John Wiley and Sons.
  5. Cowles, M. K., and Carlin, B. P. (1996). Markov chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical    Association, 91(434), 883-904.
  6. Gamerman, D., and Lopes, H. F. (2006). Markov chain Monte Carlo: stochastic     simulation for Bayesian inference, Chapman and Hall/CRC.
  7. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6), 1087-1092.
  8. Ravenzwaaij, D. V, Cassey, P., and Brown, S. D. (2018). A simple introduction to Markov Chain Monte–Carlo sampling, Psychonomic Bulletin and Review, 25(1),  143-154.
  9. Rosenberg, M. A., and Young, V. R. (1999). A Bayesian approach to understanding time series data, North American Actuarial Journal, 3(2), 130-143.
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Bhartiya Krishi Anusandhan Patrika
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    Research Article

    volume 34 issue 3-4 (september-december 2019) : 225-228,   Doi: 10.18805/BKAP176

    Application of Bayesian Inference in Time Series Analysis

    K
    Krishna Pada Sarkar
    K
    K. N. Singh
    A
    Achal Lama
    M
    Murari Kumar
    B
    Bishal Gurung
    R
    Rajeev Kumar
    V
    Vinaykumar L. N.
    1<div style="text-align: justify;">ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi-110 012</div>

    • Submitted13-01-2020|

    • Accepted27-12-2019|

    • First Online 27-03-2020|

    • doi 10.18805/BKAP176

    Cite article:- Sarkar Pada Krishna, Singh N. K., Lama Achal, Kumar Murari, Gurung Bishal, Kumar Rajeev, N. L. Vinaykumar (2020). Application of Bayesian Inference in Time Series Analysis. Bhartiya Krishi Anusandhan Patrika. 34(3): 225-228. doi: 10.18805/BKAP176.

    ABSTRACT

    Nowadays, the availability of huge computing facilities makes it easy to adopt a complex algorithm for various problem-solving. As a result, the Bayesian method of parameter estimation, which is based on Bayes’ theorem given by Thomas Bayes, gains its popularity in recent times. Time series modeling and forecasting is an important aspect of modeling. Model performance and forecasting accuracy can be improved largely using a Bayesian approach. In this article, some basic knowledge about Bayesian inference in time series has been discussed.

    REFERENCES

    1. Andrieu, C., De Freitas, N., Doucet, A., and Jordan, M. I. (2003). An introduction to MCMC for machine learning, Machine learning, 50(1-2), 5-43.
    2. Box, G. E. P., Jenkins, G. M. and Reinsel G. C. (2007). Time-Series Analysis:  and Control, 4th edition. Willey Publication.
    3. Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, 2nd Edition. Springer, New York.
    4. Congdon, P. (2007). Bayesian statistical modelling. 2nd edition, John Wiley and Sons.
    5. Cowles, M. K., and Carlin, B. P. (1996). Markov chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical    Association, 91(434), 883-904.
    6. Gamerman, D., and Lopes, H. F. (2006). Markov chain Monte Carlo: stochastic     simulation for Bayesian inference, Chapman and Hall/CRC.
    7. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6), 1087-1092.
    8. Ravenzwaaij, D. V, Cassey, P., and Brown, S. D. (2018). A simple introduction to Markov Chain Monte–Carlo sampling, Psychonomic Bulletin and Review, 25(1),  143-154.
    9. Rosenberg, M. A., and Young, V. R. (1999). A Bayesian approach to understanding time series data, North American Actuarial Journal, 3(2), 130-143.
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