This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis. The book covers a wide range of topics, including ARIMA models, forecasting methods, spectral analysis, linear systems, state-space models, the Kalman filters, nonlinear models, volatility models, and multivariate models. It also presents many examples and implementations of time series models and methods to reflect advances in the field.
Highlights of the seventh edition:
- A new chapter on univariate volatility models
- A revised chapter on linear time series models
- A new section on multivariate volatility models
- A new section on regime switching models
- Many new worked examples, with R code integrated into the text
The book can be used as a textbook for an undergraduate or a graduate level time series course in statistics. The book does not assume many prerequisites in probability and statistics, so it is also intended for students and data analysts in engineering, economics, and finance.
Table of Contents
Basic Descriptive Techniques
Some Linear Time Series Models
Fitting Time Series Models in the Time Domain
Stationary Processes in the Frequency Domain
State-Space Models and the Kalman Filter
Multivariate Time Series Modelling
Some More Advanced Topics
Appendix A Fourier, Laplace, and z-Transforms
Appendix B Dirac Delta Function
Appendix C Covariance and Correlation
Answers to Exercises
Chris Chatfield is a retired Reader in Statistics at the University of Bath, UK, the author of five books and numerous research papers, and an elected Honorary Fellow of the International Institute of Forecasters.
Haipeng Xing is an associate professor in Applied Mathematics and Statistics at the State University of New York, Stony Brook, USA, the author of two books and numerous research papers. His research interests include quantitative finance and risk management, econometrics, applied stochastic control, and sequential statistical methodology.
"Chris Chatfield has already written some popular books in statistics. Haipeng Xing is also a renowned researcher in statistics with more than 8000 citation. Efforts have been made by both authors to publish reliable data and information relating to different applications... The best part of the book is that some exercises are explained explicitly with sufficient hints. The authors also review several books on time series by other researchers from 1971 to 2010...Overall, this book is a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis in a wide range of topics. The book is intended for masters and undergraduate students in mathematics, probability, economics, statistics, astrophysics, biomedical engineering, and neuroscience. However, students who are early in a relevant PhD programme should also read this book to gain fundamental background knowledge."
- Chitaranjan Mahapatra, ISCB News, July 2020
"One would never fail to notice the accessibility and the patience with which the authors introduce each of the topics in this classical textbook. The new edition successfully continues the style of the past editions, to be the one of the most accessible textbook on time series analysis. It introduces the topics with intuitions, followed with the most necessary technical details. I am particularly pleased that the R-code and data sets accompanying all the graphical analysis throughout the book, appear right where the definitions are introduced, satisfying the immediate curiosity of the readers. Together with the homework, they provide a nice platform to engage in description, explanation, prediction and control using time series data. Many data sets are updated or newly incorporated, relative to the previous version. A must-read for anyone interested in an introduction to time series."
- Feng Yao, West Virginia University