Compression-Based Methods of Statistical Analysis and Prediction of Time Series
Authors: Ryabko, Boris, Astola, Jaakko, Malyutov, Mikhail
Free Preview- Useful for researchers and graduate students in information theory, coding, cryptography, statistics, and computational linguistics
- Topics of foundational interest
- Describes applications such as attacks on block ciphers and authorship attribution
Buy this book
- 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.
- 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)
- Table of contents (3 chapters)
-
-
Statistical Methods Based on Universal Codes
Pages 1-43
-
Applications to Cryptography
Pages 45-70
-
SCOT-Modeling and Nonparametric Testing of Stationary Strings
Pages 71-144
-
Table of contents (3 chapters)
Recommended for you
Bibliographic Information
- Bibliographic Information
-
- Book Title
- Compression-Based Methods of Statistical Analysis and Prediction of Time Series
- Authors
-
- Boris Ryabko
- Jaakko Astola
- Mikhail Malyutov
- Copyright
- 2016
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing Switzerland
- eBook ISBN
- 978-3-319-32253-7
- DOI
- 10.1007/978-3-319-32253-7
- Hardcover ISBN
- 978-3-319-32251-3
- Softcover ISBN
- 978-3-319-81234-2
- Edition Number
- 1
- Number of Pages
- IX, 144
- Number of Illustrations
- 8 b/w illustrations, 21 illustrations in colour
- Topics
