The Analysis of Time Series: An Introduction, Sixth Edition

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 inter

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