Time Series Analysis: Forecasting and Control, Fourth Edition

Contents

Preface to the Fourth Edition

Preface to the Third Edition

 1 Introduction

1.1 Five Important Practical Problems

1.1.1 Forecasting Time Series

1.1.2 Estimation of Transfer Functions

1.1.3 Analysis of Effects of Unusual Intervention Events to a System

1.1.4 Analysis of Multivariate Time Series

1.1.5 Discrete Control Systems

1.2 Stochastic and Deterministic Dynamic Mathematical Models

1.2.1 Stationary and Nonstationary Stochastic Models for Forecasting and Control

1.2.2 Transfer Function Models

1.2.3 Models for Discrete Control Systems

1.3 Basic Ideas in Model Building

1.3.1 Parsimony

1.3.2 Iterative Stages in the Selection of a Model

Part One Stochastic Models and Their Forecasting

2 Autocorrelation Function and Spectrum of Stationary Processes

2.1 Autocorrelation Properties of Stationary Models

2.1.1 Time Series and Stochastic Processes

2.1.2 Stationary Stochastic Processes

2.1.3 Positive Definiteness and the Autocovariance Matrix

2.1.4 Autocovariance and Autocorrelation Functions

2.1.5 Estimation of Autocovariance and Autocorrelation Functions

2.1.6 Standard Errors of Autocorrelation Estimates

2.2 Spectral Properties of Stationary Models

2.2.1 Periodogram of a Time Series

2.2.2 Analysis of Variance

2.2.3 Spectrum and Spectral Density Function

2.2.4 Simple Examples of Autocorrelation and Spectral Density Functions

2.2.5 Advantages and Disadvantages of the Autocorrelation and Spectral Density Functions

A2.1 Link between the Sample Spectrum and Autocovariance Function Estimate ...