© 2003 – Chapman and Hall/CRC
352 pages | 44 B/W Illus.
Since 1975, The Analysis of Time Series: An Introduction has introduced legions of statistics students and researchers to the theory and practice of time series analysis. With each successive edition, bestselling author Chris Chatfield has honed and refined his presentation, updated the material to reflect advances in the field, and presented interesting new data sets.
The sixth edition is no exception. It provides an accessible, comprehensive introduction to the theory and practice of time series analysis. The treatment covers a wide range of topics, including ARIMA probability models, forecasting methods, spectral analysis, linear systems, state-space models, and the Kalman filter. It also addresses nonlinear, multivariate, and long-memory models. The author has carefully updated each chapter, added new discussions, incorporated new datasets, and made those datasets available for download from www.crcpress.com. A free online appendix on time series analysis using R can be accessed at http://people.bath.ac.uk/mascc/TSA.usingR.doc.
Highlights of the Sixth Edition:
The analysis of time series can be a difficult topic, but as this book has demonstrated for two-and-a-half decades, it does not have to be daunting. The accessibility, polished presentation, and broad coverage of The Analysis of Time Series make it simply the best introduction to the subject available.
"… quite possibly … the most accessible introductory text on the subject. … Chatfield's is most highly recommended whether as a teaching text or one for self-instruction."
- Journal of the Royal Statistical Society, Issue 167 (4)
"This textbook is well-known for everyone who is interested in time series analysis…a substantial revision has taken place…it is an excellent textbook for undergraduate and postgraduate students, and can also be used by research workers as a reference or for self-tuition."
-Zentralblatt MATH 1050
"… there is no question that this text is the most accessible text on time series in existence…"
-Dennis Cox, Rice University
"The author's conversational style helps the reader to understand inherently difficult topics."
- Journal of Quality Technology
"This well-written book provides an excellent nontechnical introduction…"
- Journal of the American Statistical Association
"…the only book I would recommend to readers for a safe, practically minded, non-mathematical introduction to a fairly broad cross section of topics…"
- Neville Davies, Nottingham Trent University
Some Representative Time Series
Objectives of Time-Series Analysis
Approaches to Time-Series Analysis
Review of Books of Time Series
SIMPLE DESCRIPTIVE TECHNIQUES
Types of Variation
Stationary Time Series
The Time Plot
Analysing Series that Contain a Trend
Analysing Series that Contain Seasonal Variation
Autocorrelation and the Correlogram
Other Tests of Randomness
Handling Real Data
PROBABILITY MODELS FOR TIME SERIES
Stochastic Processes and their Properties
Some Properties of the Autocorrelation Function
Some Useful Models
The Wold Decomposition Theorem
FITTING TIME-SERIES MODELS (IN THE TIME DOMAIN)
Estimating the Autocovariance and Autocorrelation Functions
Fitting an Autoregressive Process
Fitting a Moving Average Process
Estimating the Parameters of an ARMA Model
Estimating the Parameters of an ARIMA Model
The Box-Jenkins Seasonal (SARIMA) Model
General Remarks on Model Building
A Comparative Review of Forecasting Procedures
STATIONARY PROCESSES IN THE FREQUENCY DOMAIN
The Spectral Distribution Function
The Spectral Density Function
The Spectrum of a Continuous Process
Derivation of Selected Spectra
A Simple Sinusoidal Model
Spectral Analysis: some Consistent Estimation Procedures
Confidence Intervals for the Spectrum
A Comparison of Different Estimation Procedures
Analysing a Continuous Time Series
Examples and Discussion
The Cross-Covariance and Cross-Correlation Functions
Linear systems in the Time Domain
Linear Systems in the Frequency Domain
Identification of Linear Systems
STATE-SPACE MODELS AND THE KALMAN FILTER
The Kalman Filter
Some Models with Nonlinear Structure
Models for Changing Variance
MULTIVARIATE TIME-SERIES MODELLING
Single Equation Models
Vector Autoregressive Models
Vector ARMA Models
Fitting VAR and VARMA Models
SOME MORE ADVANCED TOPICS
Model Identification Tools
Modelling Non-Stationary Series
Fractional Differencing and Long-Memory Models
Testing for Unit Roots
The Effect of Model Uncertainty
EXAMPLES AND PRACTICAL ADVICE
More on the Time Plot
Data Sources and Exercises
The Fourier, Laplace, and z-Transforms
The Dirac Delta Function
Covariance and Correlation
Some MINITAB and S-PLUS Commands
ANSWERS TO EXERCISES