Skip to main content
SpringerLink
Log in

The Application of Bayesian Estimation for the Prediction of Economic Trends

  • Article
  • Published:
The Review of Socionetwork Strategies Aims and scope Submit manuscript

Abstract

Since economic trends have a great influence on corporate activities, predicting whether the economy is in an expansion period or in retreat is important. Business condition indexes used in Japan that quantify the economy include the diffusion index (DI) and the composite index (CI). A method for predicting economic judgement is presented in this study. An economic trend is taken as an objective function and the DI and CI values are explanatory variables. The prediction model is defined as a Bayesian network. In Bayesian networks, random variables are used as nodes, and the dependency between variables is represented by a directed graph. Japan’s economic trends and DI and CI values from 1985 to 2020 are taken as experimental data. The forecast model is determined using the data from 1985 to 2017 as learning data, and the economic trend from 2018 to 2020 is predicted. The proposed algorithm is compared with a linear model for time-series data. The proposed algorithm shows better accuracy than the linear models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Robinson, R. W. (1977). Counting Unlabeled Acyclic Digraphs. Lecture Notes in Mathematics, 622, 28–43.

    Article  Google Scholar 

  2. Klenke, A. (2008). Probability Theory: A Comprehensive Course, Springer

  3. Bookstaber, R. (1985). The complete investment book: Trading stocks, bonds, and options with computer applications. Scott Foresman Trade

  4. Whittle, P. (1983). Prediction and regulation by linear least-square methods. University of Minnesota Press, ISBN 0-8166-1148-3.

  5. Hannan, E. J., & Deistler, M. (1998). Statistical theory of linear systems. Wiley series in probability and mathematical statistics. Wiley.

  6. Brockwell, P. J., & Davis, R. A. (2009) Time Series: Theory and Methods (2nd ed.), Springer. ISBN 9781441903198.

  7. Box, G., Jenkins, G. M., & Reinsel, G. (1994). Time series analysis: Forecasting and control. Prentice-Hall

  8. Sasaki, H., & Urano, M. (2020). Economic Trend Index Forecasting Using Multiple Regression Model and Random Forest, Proceedings of The 34th Annual Conference of the Japanese Society for Artificial Intelligence, Vol.JSAI2020, 2I1GS202-2I1GS202

  9. Sakaji, H., Sakai, H., & Masuyama, S. (2007). Extraction and analysis of basis expressions that indicate economic trends. Information Processing Society of Japan, Special Interest Group of Natural Language Processing (IPSJ-SIGNL), 0919-6072, Vol. 180, 151–156.

  10. Kaku, K., & Neki, S. (2014). Study of Economic interconnectedness analysis in Pacific Rim’s Countries by Using Stock index. Proceedings of the School of Information and Telecommunication Engineering Tokai University, 7(2), 44–51 ((in Japanese)).

    Google Scholar 

  11. Pearl, J. (1985). Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning, (UCLA Technical Report CSD-850017). Proceedings of the 7th Conference of the Cognitive Science Society, University of California, Irvine, CA. pp. 329-334. Retrieved 2009-05-01

  12. Neapolitan, R. E. (1985). Probabilistic reasoning in expert systems: Theory and algorithms. Wiley. ISBN 978-0-471-61840-9.

  13. Pearl, J. (1988) Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann. ISBN 978-1558604797.

  14. Cooper, G. F. (1990). The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence, 42(2–3), 393–405. https://doi.org/10.1016/0004-3702(90)90060-d.

    Article  Google Scholar 

  15. Heckerman, D., Geiger, D., & Chickering, D. (1995). Learning Bayesian Networks : The Combination of Knowledge and Statistical Data. Machine Learning, 20, 197–243.

    Article  Google Scholar 

  16. Neapolitan, R. E. (2004). Learning Bayesian networks. Prentice Hall. ISBN 978-0-13-012534-7.

  17. Zuo, Y., & Kita, E. (2012). Stock price forecast using Bayesian network. Expert Systems with Applications, 39(8), 6729–6737.

    Article  Google Scholar 

  18. Hannan, E. J. (1970). Multiple time series. Wiley series in probability and mathematical statistics. John Wiley and Sons

  19. Brockwell, P. J., & Davis, R. A. (2009). Time series: Theory and methods. Springer

  20. Terasvirta, T., Tjostheim, D., & Granger, C. W. J. (2011). Modeling nonlinear economic time series. Oxford University Press

Download references

Author information

Authors and Affiliations

  1. Graduate School of Informatics, Nagoya University, Nagoya, Japan

    Norimasa Nakamura & Eisuke Kita

Authors
  1. Norimasa Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eisuke Kita
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eisuke Kita.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nakamura, N., Kita, E. The Application of Bayesian Estimation for the Prediction of Economic Trends. Rev Socionetwork Strat 16, 239–258 (2022). https://doi.org/10.1007/s12626-022-00114-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12626-022-00114-y

Keywords

Search

Navigation