Compression-Based Methods of Statistical Analysis and Prediction of Time Series

  • Boris Ryabko
  • Jaakko Astola
  • Mikhail Malyutov

Table of contents

  1. Front Matter
    Pages i-ix
  2. Boris Ryabko, Jaakko Astola, Mikhail Malyutov
    Pages 1-43
  3. Boris Ryabko, Jaakko Astola, Mikhail Malyutov
    Pages 45-70
  4. Boris Ryabko, Jaakko Astola, Mikhail Malyutov
    Pages 71-144

About this book


Universal codes efficiently compress sequences generated by stationary and ergodic sources with unknown statistics, and they were originally designed for lossless data compression. In the meantime, it was realized that they can be used for solving important problems of prediction and statistical analysis of time series, and this book describes recent results in this area.

The first chapter introduces and describes the application of universal codes to prediction and the statistical analysis of time series; the second chapter describes applications of selected statistical methods to cryptography, including attacks on block ciphers; and the third chapter describes a homogeneity test used to determine authorship of literary texts.

The book will be useful for researchers and advanced students in information theory, mathematical statistics, time-series analysis, and cryptography. It is assumed that the reader has some grounding in statistics and in information theory.


Universal Codes Forecasting Data Compression Stream Ciphers Random-Number Generators Block Ciphers SCOT-Modeling Homogeneity Testing Text Comparison Authorship Attribution

Authors and affiliations

  • Boris Ryabko
    • 1
  • Jaakko Astola
    • 2
  • Mikhail Malyutov
    • 3
  1. 1.Inst. of Computational TechnologiesSiberian Branch Russian Acad. of ScienceNovosibirskRussia
  2. 2.Dept. of Signal ProcessingTampere University of TechnologyTampereFinland
  3. 3.Dept. of MathematicsNortheastern UniversityBostonUSA

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-32251-3
  • Online ISBN 978-3-319-32253-7
  • Buy this book on publisher's site