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Compression-Based Methods of Time Series Forecasting

Author

Listed:
  • Konstantin Chirikhin

    (Federal Research Center for Information and Computational Technologies, 630090 Novosibirsk, Russia
    Department of Information Technologies, Novosibirsk State University, 630090 Novosibirsk, Russia)

  • Boris Ryabko

    (Federal Research Center for Information and Computational Technologies, 630090 Novosibirsk, Russia
    Department of Information Technologies, Novosibirsk State University, 630090 Novosibirsk, Russia)

Abstract

Time series forecasting is an important research topic with many practical applications. As shown earlier, the problems of lossless data compression and prediction are very similar mathematically. In this article, we propose several forecasting methods based on real-world data compressors. We consider predicting univariate and multivariate data, describe how multiple data compressors can be combined into one forecasting method with automatic selection of the best algorithm for the input data. The developed forecasting techniques are not inferior to the known ones. We also propose a way to reduce the computation time of the combined method by using the so-called time-universal codes. To test the proposed techniques, we make predictions for real-world data such as sunspot numbers and some social indicators of Novosibirsk region, Russia. The results of our computations show that the described methods find non-trivial regularities in data, and time universal codes can reduce the computation time without losing accuracy.

Suggested Citation

  • Konstantin Chirikhin & Boris Ryabko, 2021. "Compression-Based Methods of Time Series Forecasting," Mathematics, MDPI, vol. 9(3), pages 1-11, January.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:3:p:284-:d:490590
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    References listed on IDEAS

    as
    1. Ashton de Silva & Rob J. Hyndman & Ralph D. Snyder, 2007. "The vector innovation structural time series framework: a simple approach to multivariate forecasting," Monash Econometrics and Business Statistics Working Papers 3/07, Monash University, Department of Econometrics and Business Statistics.
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