This volume provides a unified mathematical introduction to stationary time series models and to continuous time stationary stochastic processes. The analysis of these stationary models is carried out in time domain and in frequency domain. It begins with a practical discussion on stationarity, by which practical methods for obtaining stationary data are described. The presented topics are illustrated by numerous examples. Readers will find the following covered in a comprehensive manner:
At the end, some selected topics such as stationary random fields, simulation of Gaussian stationary processes, time series for planar directions, large deviations approximations and results of information theory are presented. A detailed appendix containing complementary materials will assist the reader with many technical aspects of the book.
Contents:
Introduction:
Stationary Stochastic Models and Outline
Fourier Analysis
Stationary Time Series:
Introduction
ARMA Time Series
Autocovariance and Related Functions
Analysis in Frequency Domain
Further Classical Topics on Time Series
Stationary Processes with Continuous Time:
Introduction
Important Stochastic Processes
Mean Square Properties of Stationary Processes
Stochastic Integrals
Spectral Distribution and Autocovariance Function
Spectral Decomposition of Stationary Processes and the Spectral Theorem
Spectral Analysis of Gaussian Processes
Spectral Analysis of Counting Processes
Time Invariant Linear Filters
Selected Topics on Stationary Models:
Stationary Random Fields
Circular Time Series
Long Range Dependence
Nonintegrable Spectral Density and Intrinsic Stationarity
Unstable System
Hilbert Transform and Envelope
Simulation of Stationary Gaussian Processes
Large Deviations Theory for Time Series
Information Theoretic Results for Time Series
Appendices:
Mathematical Complements
Abbreviations, Mathematical Notation and Data
Readership: Upper-level undergraduate and graduate students, for lectures on time series or on stochastic processes with continuous time. Researchers in academia and applied scientists in the industry, in the field of time series or stationary processes. These lectures can be given to students of mathematics or statistics as well as to students from other technical fields, at Bachelor's upper-level and at Master's level. Key Features:
The topics are presented progressively, by going from discrete to continuous time, by studying connected questions and by completing abstract theory with compelling examples
Suited for upper-level undergraduate and graduate students, researchers in academia and scientists in industry
Covers both fundamentals and practical aspects of stationary models
This book provides a unified presentation of stationary time series and continuous time stationary processes
The appendix provides complementary material that assists the reader with technical aspects of the book