The Analysis of Time Series: An Introduction, Sixth Edition (Texts in Statistical Science)
By Chris Chatfield
Publisher: Chapman & Hall/CRC
Number Of Pages: 352
Publication Date: 2003-07-29
ISBN-10 / ASIN: 1584883170
ISBN-13 / EAN: 9781584883173
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, best-selling 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. Highlights of the Sixth Edition:·A new section on Handling Real Data·New discussion on prediction intervals·A completely revised and restructured chapter on more advanced topics, with new material on the aggregation of time series, analyzing time series in finance, and discrete-valued time series·A new chapter of Examples and Practical Advice·Thorough updates and revisions throughout the text that reflect recent developments and dramatic changes in computing practices over the last few yearsThe 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.
Contents
Preface to the Sixth Edition xi
Abbreviations and Notation xiii
1 Introduction
1.1 Some Representative Time Series
1.2 Terminology
1.3 Objectives of Time-Series Analysis
1.4 Approaches to Time-Series Analysis
1.5 Review of Books on Time Series
2 Simple Descriptive Techniques
2.1 Types of Variation
2.2 Stationary Time Series
2.3 The Time Plot
2.4 Transformations
2.5 Analysing Series that Contain a Trend
2.6 Analysing Series that Contain Seasonal Variation
2.7 Autocorrelation and the Correlogram
2.8 Other Tests of Randomness
2.9 Handling Real Data
3 Some Time-Series Models
3.1 Stochastic Processes and Their Properties
3.2 Stationary Processes
3.3 Some Properties of the Autocorrelation Function
3.4 Some Useful Models
3.5 The Wold Decomposition Theorem
4 Fitting Time-Series Models in the Time Domain
4.1 Estimating Autocovariance and Autocorrelation Functions
4.2 Fitting an Autoregressive Process
4.3 Fitting a Moving Average Process
4.4 Estimating Parameters of an ARMA Model
4.5 Estimating Parameters of an ARIMA Model
4.6 Box-Jenkins Seasonal ARIMA Models
4.7 Residual Analysis
4.8 General Remarks on Model Building
5 Forecasting
5.1 Introduction
5.2 Univariate Procedures
5.3 Multivariate Procedures
5.4 Comparative Review of Forecasting Procedures
5.5 Some Examples
5.6 Prediction Theory
6 Stationary Processes in the Frequency Domain
6.1 Introduction
6.2 The Spectral Distribution Function
6.3 The Spectral Density Function
6.4 The Spectrum of a Continuous Process
6.5 Derivation of Selected Spectra
7 Spectral Analysis
7.1 Fourier Analysis
7.2 A Simple Sinusoidal Model
7.3 Periodogram Analysis
7.4 Some Consistent Estimation Procedures
7.5 Confidence Intervals for the Spectrum
7.6 Comparison of Different Estimation Procedures
7.7 Analysing a Continuous Time Series
7.8 Examples and Discussion
8 Bivariate processes
8.1 Cross-Covariance and Cross-Correlation
8.2 The Cross-Spectrum
9 Linear Systems
9.1 Introduction
9.2 Linear Systems in the Time Domain
9.3 Linear Systems in the Frequency Domain
9.4 Identification of Linear Systems
10 State-Space Models and the Kalman Filter
10.1 State-Space Models
10.2 The Kalman Filter
11 Non-Linear Models
11.1 Introduction
11.2 Some Models with Non-Linear Structure
11.3 Models for Changing Variance
11.4 Neural Networks
11.5 Chaos
11.6 Concluding Remarks
11.7 Bibliography
12 Multivariate Time-Series Modelling
12.1 Introduction
12.2 Single Equation Models
12.3 Vector Autoregressive Models
12.4 Vector ARMA Models
12.5 Fitting VAR and VARMA Models
12.6 Co-integration
12.7 Bibliography
13 Some More Advanced Topics
13.1 Model Identification Tools
13.2 Modelling Non-Stationary Series
13.3 Fractional Differencing and Long-Memory Models
13.4 Testing for Unit Roots
13.5 Model Uncertainty
13.6 Control Theory
13.7 Miscellanea
14 Examples and Practical Advice
14.1 General Comments
14.2 Computer Software
14.3 Examples
14.4 More on the Time Plot
14.5 Concluding Remarks
14.6 Data Sources and Exercises
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