Application of time-universal codes to time series forecasting

Konstantin Chirikhin

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

As shown in previous research, data compression techniques can be successfully used in time series forecasting. The problem is that there exist many different data compression algorithms and it's unknown in advance which one will be the best for predicting a specific time series. In this study, we use an approach known as time-universal data compression to quickly select a close to optimal algorithm. Its basic idea is to compress only a part of the input data using each of the available compressors in order to select the best one. Then the data is compressed using the selected algorithm only. We implemented this approach and used it to predict real-world data such as sunspot numbers and the ionospheric T-index. The results of our computations show that the approach is quite effective and can be useful in practice.

Original languageEnglish
Title of host publicationModelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020
EditorsAlexandre Nketsa, Claude Baron, Clement Foucher
PublisherEUROSIS
Pages60-63
Number of pages4
ISBN (Electronic)9789492859129
Publication statusPublished - 2020
Event34th Annual European Simulation and Modelling Conference, ESM 2020 - Toulouse, France
Duration: 21 Oct 202023 Oct 2020

Publication series

NameModelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020

Conference

Conference34th Annual European Simulation and Modelling Conference, ESM 2020
CountryFrance
CityToulouse
Period21.10.202023.10.2020

Keywords

  • Data compression
  • Time series forecasting
  • Universal coding

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