时间序列分析 预测与控制 英文版 第3版

1 (p1): 1 INTRODUCTION
2 (p1-1): 1.1 Four Important Practical Problems
2 (p1-1-1): 1.1.1 Forecasting Time Series
3 (p1-1-2): 1.1.2 Estimation of Transfer Functions
4 (p1-1-3): 1.1.3 Analysis of Effects of Unusual Intervention Events To a System
5 (p1-1-4): 1.1.4 Discrete Control Systems
7 (p1-2): 1.2 Stochastic and Deterministic Dynamic Mathematical Models
7 (p1-2-1): 1.2.1 Stationary and Nonstationary Stochastic Models for Forecasting and Control
12 (p1-2-2): 1.2.2 Transfer Function Models
14 (p1-2-3): 1.2.3 Models for Discrete Control Systems
16 (p1-3): 1.3 Basic Ideas in Model Building
16 (p1-3-1): 1.3.1 Parsimony
16 (p1-3-2): 1.3.2 Iterative Stages in the Selection of a Model
19 (p2): Part 1 Stochastic Models and Their Forecasting
21 (p2-1): 2 AUTOCORRELATION FUNCTION AND SPECTRUM OF STATIONARY PROCESSES
21 (p2-1-1): 2.1 Autocorrelation Properties of Stationary Models
21 (p2-1-1-1): 2.1.1 Time Series and Stochastic Processes
23 (p2-1-1-2): 2.1.2 Stationary Stochastic Processes
26 (p2-1-1-3): 2.1.3 Positive Definiteness and the Autocovariance Matrix
29 (p2-1-1-4): 2.1.4 Autocovariance and Autocorrelation Functions
30 (p2-1-1-5): 2.1.5 Estimation of Autocovariance and Autocorrelation Functions
32 (p2-1-1-6): 2.1.6 Standard Error of Autocorrelation Estimates
35 (p2-1-2): 2.2 Spectral Properties of Stationary Models
35 (p2-1-2-1): 2.2.1 Periodogram of a Time Series
36 (p2-1-2-2): 2.2.2 Analysis of Variance
37 (p2-1-2-3): 2.2.3 Spectrum and Spectral Density Function
41 (p2-1-2-4): 2.2.4 Simple Examples of Autocorrelation and Spectral Density Functions
43 (p2-1-2-5): 2.2.5 Advantages and Disadvantages of the Autocorrelation and Spectral Density Functions
44 (p2-1-3): A2.1 Link Between the Sample Spectrum and Autocovariance Function Estimate
46 (p2-2): 3 LINEAR STATIONARY MODELS
46 (p2-2-1): 3.1 General Linear Process
46 (p2-2-1-1): 3.1.1 Two Equivalent Forms for the Linear Process
49 (p2-2-1-2): 3.1.2 Autocovariance Generating Function of a Linear Process
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