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| Preface to the Sixth Edition | |
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| Abbreviations and Notation | |
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| Introduction | |
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| Some Representative Time Series | |
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| Terminology | |
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| Objectives of Time-Series Analysis | |
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| Approaches to Time-Series Analysis | |
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| Review of Books on Time Series | |
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| Simple Descriptive Techniques | |
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| Types of Variation | |
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| Stationary Time Series | |
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| The Time Plot | |
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| Transformations | |
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| Analysing Series that Contain a Trend | |
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| Analysing Series that Contain Seasonal Variation | |
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| Autocorrelation and the Correlogram | |
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| Other Tests of Randomness | |
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| Handling Real Data | |
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| Some Time-Series Models | |
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| Stochastic Processes and Their Properties | |
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| Stationary Processes | |
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| Some Properties of the Autocorrelation Function | |
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| Some Useful Models | |
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| The Wold Decomposition Theorem | |
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| Fitting Time-Series Models in the Time Domain | |
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| Estimating Autocovariance and Autocorrelation Functions | |
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| Fitting an Autoregressive Process | |
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| Fitting a Moving Average Process | |
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| Estimating Parameters of an ARMA Model | |
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| Estimating Parameters of an ARIMA Model | |
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| Box-Jenkins Seasonal ARIMA Models | |
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| Residual Analysis | |
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| General Remarks on Model Building | |
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| Forecasting | |
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| Introduction | |
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| Univariate Procedures | |
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| Multivariate Procedures | |
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| Comparative Review of Forecasting Procedures | |
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| Some Examples | |
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| Prediction Theory | |
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| Stationary Processes in the Frequency Domain | |
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| Introduction | |
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| The Spectral Distribution Function | |
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| The Spectral Density Function | |
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| The Spectrum of a Continuous Process | |
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| Derivation of Selected Spectra | |
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| Spectral Analysis | |
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| Fourier Analysis | |
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| A Simple Sinusoidal Model | |
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| Periodogram Analysis | |
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| Some Consistent Estimation Procedures | |
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| Confidence Intervals for the Spectrum | |
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| Comparison of Different Estimation Procedures | |
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| Analysing a Continuous Time Series | |
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| Examples and Discussion | |
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| Bivariate processes | |
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| Cross-Covariance and Cross-Correlation | |
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| The Cross-Spectrum | |
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| Linear Systems | |
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| Introduction | |
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| Linear Systems in the Time Domain | |
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| Linear Systems in the Frequency Domain | |
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| Identification of Linear Systems | |
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| State-Space Models and the Kalman Filter | |
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| State-Space Models | |
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| The Kalman Filter | |
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| Non-Linear Models | |
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| Introduction | |
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| Some Models with Non-Linear Structure | |
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| Models for Changing Variance | |
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| Neural Networks | |
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| Chaos | |
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| Concluding Remarks | |
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| Bibliography | |
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| Multivariate Time-Series Modelling | |
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| Introduction | |
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| Single Equation Models | |
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| Vector Autoregressive Models | |
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| Vector ARMA Models | |
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| Fitting VAR and VARMA Models | |
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| Co-integration | |
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| Bibliography | |
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| Some More Advanced Topics | |
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| Model Identification Tools | |
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| Modelling Non-Stationary Series | |
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| Fractional Differencing and Long-Memory Models | |
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| Testing for Unit Roots | |
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| Model Uncertainty | |
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| Control Theory | |
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| Miscellanea | |
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| Examples and Practical Advice | |
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| General Comments | |
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| Computer Software | |
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| Examples | |
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| More on the Time Plot | |
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| Concluding Remarks | |
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| Data Sources and Exercises | |
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| Fourier, Laplace and z-Transforms | |
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| Dirac Delta Function | |
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| Covariance and Correlation | |
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| Some MINITAB and S-PLUS Commands | |
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| Answers to Exercises | |
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| References | |
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| Index | |