Compression-based Methods Of Statistical Analysis And Prediction Of Time Series by Boris RyabkoCompression-based Methods Of Statistical Analysis And Prediction Of Time Series by Boris Ryabko

Compression-based Methods Of Statistical Analysis And Prediction Of Time Series

byBoris Ryabko, Jaakko Astola, Mikhail Malyutov

Hardcover | May 27, 2016

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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.

Title:Compression-based Methods Of Statistical Analysis And Prediction Of Time SeriesFormat:HardcoverDimensions:144 pages, 23.5 × 15.5 × 0.25 inPublished:May 27, 2016Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3319322516

ISBN - 13:9783319322513

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Reviews

Table of Contents

Statistical Methods Based on Universal Codes.- Applications to Cryptography.- SCOT-Modeling and Nonparametric Testing of Stationary Strings.

Editorial Reviews

"The book under review describes several recent results on Universal Codes. . its reading may be useful for non-mathematical professionals interested in handling large data sources." (Oscar Bustos, zbMATH 1360.94001, 2017)