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: 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.

Table of Contents:
INTRODUCTION Some Representative Time Series Terminology 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 Transformation 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 Stationary Processes 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 Residual Analysis General Remarks on Model Building FORECASTING Introduction Univariate Procedures Multivariate Procedures A Comparative Review of Forecasting Procedures Some Examples Prediction Theory STATIONARY PROCESSES IN THE FREQUENCY DOMAIN Introduction The Spectral Distribution Function The Spectral Density Function The Spectrum of a Continuous Process Derivation of Selected Spectra SPECTRAL ANALYSIS Fourier Analysis A Simple Sinusoidal Model Periodogram Analysis 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 BIVARIATE PROCESSES The Cross-Covariance and Cross-Correlation Functions The Cross-Spectrum LINEAR SYSTEMS Introduction Linear systems in the Time Domain Linear Systems in the Frequency Domain Identification of Linear Systems STATE-SPACE MODELS AND THE KALMAN FILTER State-Space Models The Kalman Filter NON-LINEAR MODELS Introduction Some Models with Nonlinear Structure Models for Changing Variance Neural Networks Chaos Concluding Remarks Bibliography MULTIVARIATE TIME-SERIES MODELLING Introduction Single Equation Models Vector Autoregressive Models Vector ARMA Models Fitting VAR and VARMA Models Co-integration Bibliography 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 Control Theory Miscellanea EXAMPLES AND PRACTICAL ADVICE General Comments Computer Software Examples More on the Time Plot Concluding Remarks Data Sources and Exercises APPENDICES The Fourier, Laplace, and z-Transforms The Dirac Delta Function Covariance and Correlation Some MINITAB and S-PLUS Commands ANSWERS TO EXERCISES REFERENCES