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Markov Chains application to the financial-economic time series prediction

Author

Listed:
  • Vladimir Soloviev
  • Vladimir Saptsin
  • Dmitry Chabanenko

Abstract

In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of aftereffect or memory. The technology proposes prediction with the hierarchy of time discretization intervals and splicing procedure for the prediction results at the different frequency levels to the single prediction output time series. The hierarchy of time discretizations gives a possibility to use fractal properties of the given time series to make prediction on the different frequencies of the series. The prediction results for world's stock market indices is presented.

Suggested Citation

  • Vladimir Soloviev & Vladimir Saptsin & Dmitry Chabanenko, 2011. "Markov Chains application to the financial-economic time series prediction," Papers 1111.5254, arXiv.org.
  • Handle: RePEc:arx:papers:1111.5254
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    File URL: http://arxiv.org/pdf/1111.5254
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    References listed on IDEAS

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    Cited by:

    1. Stepchenko Arthur & Chizhov Jurij, 2015. "Applying Markov Chains for NDVI Time Series Forecasting of Latvian Regions," Information Technology and Management Science, De Gruyter Open, vol. 18(1), pages 57-61, December.
    2. repec:eee:apmaco:v:303:y:2017:i:c:p:226-239 is not listed on IDEAS

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