The Analysis of Time Series: An Introduction, Sixth Edition (Chapman & Hall/CRC Texts in Statistical Science)

Main subject categories: • Time series analysis • Econometrics • Dynamical systems and ergodic theory • Inference from stochastic processes • Statistics • Game theory, economics, finance, and other social and behavioral sciencesSince 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 years. 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. Preface to fifth edition Abbreviations and notation 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 scries 2.3 The time plot 2.4 Transformations 2.5 Analysing scries which contain a trend 2.6 Analysing series which contain seasonal variation 2.7 Autocorrelation 2.8 Other tests of randomness Exercises 3 Probability models for time series 3.1 Stochastic processes 3.2 Stationary processes 3.3 The autocorrelation function 3.4 Some useful stochastic processes 3.5 The Wold decomposition theorem Exercises 4 Estimation in the time domain 4.1 Estimating the autocovariance and autocorrelation functions 4.2 Fitting an autoregressive process 4.3 Fitting a moving average process 4.4 Estimating the parameters of an ARMA model 4.5 Estimating the parameters of an ARIMA model 4.6 The Box-Jenkins seasonal (SARIMA) model 4.7 Residual analysis 4.8 General remarks on model building Exercises 5 Forecasting 5.1 Introduction 5.2 Univariate procedures 5.3 Multivariate procedures 5.4 A comparative review of forecasting procedures 5.5 Some examples 5.6 Prediction theory Exercises 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 Exercises 7 Spectral analysts 7.1 Fourier analysis 7.2 A simple sinusoidal model 7.3 Periodogram analysis 7.4 Spectral analysis: some consistent estimation procedures 7.5 Confidence intervals for the spectrum 7.6 A comparison of different estimation procedures 7.7 Analysing a continuous time series 7.8 Discussion Exercises 8 Bivariate processes 8.1 Cross-covariance and cross-correlation functions 8.2 The cross-spectrum Exercises 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 Exercises 10 Slate-space models and the Kalman filter 10.1 State-space models 10.2 The Kalman filter Exercises 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 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 13 Some other topics 13.1 Model identification tools 13.2 Modelling non-stationary series 13.3 The effect of model uncertainty 13.4 Control theory 13.5 Miscellanea Appendix A The Fourier, Laplace and Z transforms Appendix В The Dirac delta function Appendix C Covariance Appendix D Some worked examples References Answers to exercises Author index Subject index "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. The sixth edition 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 at www.crcpress.com."--Jacket. 1. Introduction -- 2. Simple Descriptive Techniques -- 3. Probability Models For Time Series -- 4. Estimation In The Time Domain -- 5. Forecasting -- 6. Stationary Processes In The Frequency Domain -- 7. Spectral Analysis -- 8. Bivariate Processes -- 9. Linear Systems -- 10. State-space Models And The Kalman Filter -- 11. Non-linear Models -- 12. Multivariate Time-series Modelling -- 13. Some Other Topics -- Appendix A The Fourier, Laplace And Z Transforms -- Appendix B The Dirac Delta Function -- Appendix C Covariance -- Appendix D Some Worked Examples. C. Chatfield. Includes Bibliographical References (p. [262]-270) And Indexes. This non-technical text introduces a broad cross-section of topics in time series analysis. This edition includes three new chapters, providing material on non-linear models, multivariate models, and other topics such as model uncertainty, wavelets and fractional differencing «As an introduction to techniques for analyzing discrete time series, this textbook explains probability models, the spectral density function, time-invariant linear systems, state-space models, nonlinear models, and multivariate time series models.» — «Book News, Inc.» This edition includes additional chapters on non-linear models (incorporating material on neural nets and chaos) and multivariate models (including VARMA models) as well as additional examples and references to topics such as wavelets and long-memory models A wide range of topics is covered including ARIMA probability models, forecasting methods (including exponential smoothing and Box-Jenkins), spectral analysis, linear systems, state-space models and the Kalman filter