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

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

• Inst. of Computational Technologies, Siberian Branch Russian Acad. of Science, Novosibirsk, Russia

  Boris Ryabko

• Dept. of Signal Processing, Tampere University of Technology, Tampere, Finland

  Jaakko Astola

• Dept. of Mathematics, Northeastern University, Boston, USA

  Mikhail Malyutov

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