Compression-based Methods Of Statistical Analysis And Prediction Of Time Series
by Jaakko Astola /
2016 / English / PDF
2.5 MB Download
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.
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 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.
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.
