Compression-based methods of time series forecasting

Konstantin Chirikhin, Boris Ryabko

Research output: Contribution to journalArticlepeer-review

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 mathemati-cally. 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.

Original languageEnglish
Article number284
Pages (from-to)1-11
Number of pages11
JournalMathematics
Volume9
Issue number3
DOIs
Publication statusPublished - 1 Feb 2021

Keywords

  • Artificial intelligence
  • Data compression
  • Time series forecasting
  • Universal coding

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