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Time series prediction based on data compression methods

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Abstract

We propose efficient (“fast” and low memory consuming) algorithms for universal-coding-based prediction methods for real-valued time series. Previously, for such methods it was only proved that the prediction error is asymptotically minimal, and implementation complexity issues have not been considered at all. The provided experimental results demonstrate high precision of the proposed methods.

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Author information

Authors and Affiliations

  1. Novosibirsk State University, Novosibirsk, Russia

    A. S. Lysyak & B. Ya. Ryabko

  2. Institute of Computational Technologies, Siberian Branch of the, Novosibirsk, Russia

    B. Ya. Ryabko

Authors
  1. A. S. Lysyak
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  2. B. Ya. Ryabko
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Corresponding author

Correspondence to A. S. Lysyak.

Additional information

Original Russian Text © A.S. Lysyak, B.Ya. Ryabko, 2016, published in Problemy Peredachi Informatsii, 2016, Vol. 52, No. 1, pp. 101–109.

Supported in part by the Russian Foundation for Basic Research, project no. 15-07-01851.

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Lysyak, A.S., Ryabko, B.Y. Time series prediction based on data compression methods. Probl Inf Transm 52, 92–99 (2016). https://doi.org/10.1134/S0032946016010075

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  • DOI: https://doi.org/10.1134/S0032946016010075

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