在这本全英文的时间序列分析经典之作中，几位统计大家用极其通俗的语言，运用大量的实例，深入浅出而又形象地阐明时间序列分析的精髓，使读者免去过多繁杂的数学公式推导证明，而很快掌握实践的技巧，体会其中直观而深刻的思想。相信每一位研读此书的读者都会获益匪浅。

本书自1970年初版以来，不断修订再版，以其经典性和权威性成为有关时间序列分析领域书籍的典范。书中涉及时间序列随机(统计)模型的建立及许多重要的应用领域的使用，包括预测，模型的描述、估计、识别和诊断，动态关系的传递函数的识别、拟合及检验，干预事件影响的建模和过程控制等专题。本书叙述简明，强调实际技术，配有大量实例。

本书可作为统计和相关专业高年级本科生或研究生教材，也可以作为统计专业技术人员的参考书。

1 INTRODUCTION

1.1  Four Important Practical Problems 2

     1.1.1 Forecasting Time Series, 2

     1.1.2 Estimation of Transfer Functions, 3

     1.1.3 Analysis of Effects of Unusual Intervention Events

         To a System, 4

     1.1.4 Discrete Control Systems, 5

1.2  Stochastic and Deterministic Dynamic Mathematical

     Models 7

     1.2.1 Stationary and Nonstationary Stochastic Models

         for Forecasting and Control, 7

     1.2.2 Transfer Function Models, 12

     1.2.3 Models for Discrete Control Systems, 14

1.3  Basic Ideas in Model Building 16

     1.3.1 Parsimony, 16

     1.3.2 Iterative Stages in the Selection of a Model, 16

Part I Stochastic Models and Their Forecasting

2 AUTOCORRELATION FUNCTION AND SPECTRUM OF

 STATIONARY PROCESSES

2.1  Autocorrelation Properties of Stationary Models 21

     2.1.1 Time Series and Stochastic Processes, 21

     2.1.2 Stationary Stochastic Processes, 23

     2.1.3 Positive Definiteness and the Autocovariance

          Matrix, 26

     2.1.4 Autocovariance and Autocorrelation Functions, 29

     2.1.5 Estimation of Autocovariance and Autocorrelation

          Functions, 30

     2.1.6 Standard Error of Autocorrelation Estimates, 32

2.2  Spectral Properties of Stationary Models 35

     2.2.1 Periodogram of a Time Series, 35

     2.2.2 Analysis of Variance, 36

    2.2.3 Spectrum and Spectral Density Function, 37

    2.2.4 Simple Examples of Autocorrelation and Spectral

         Density Functions, 41

    2.2.5 Advantages and Disadvantages of the

         Autocorrelation and Spectral Density Functions, 43

 A2.1  Link Between the Sample Spectrum and

     Autocovariance Function Estimate 44

3 LINEAR STATIONARY MODELS

 3,1  General Linear Process 46

    3.1.1 Two Equivalent Forms for the Linear Process, 46

    3.1.2 Autocovariance Generating Function of a Linear

         Process, 49

    3.1.3 Stationarity and lnvertibility Conditions for a

         Linear Process, 50

    3.1.4 Autoregressive and Moving Average Processes, 52

 3.2  Autoregressive Processes 54

    3.2.1 Stationarity Conditions for Autoregressive

         Processes, 54

    3.2.2 Autocorrelation Function and Spectrum of

         Autoregressive Processes, 55

    3.2.3 First-Order Autoregressive (Markov) Process, 58

    3.2.4 Second-Order Autoregressive Process, 60

    3.2.5 Partial Autocorrelation Function, 64

    3.2.6 Estimation of the Partial Autocorrelation Function,67

    3.2.7 Standard Errors of Partial Autocorrelation

         Estimates, 68

 3.3  Moving Average Processes 69

    3.3.1 lnvertibility Conditions for Moving Average

         Processes, 69

    3.3.2 Autocorrelation Function and Spectrum of Moving

         Average Processes, 70

    3.3.3 First-Order Moving Average Process, 72

    3.3.4 Second-Order Moving Average Process, 73

    3.3.5 Duality Between Autoregressive and Moving

         Average Processes, 75

  3.4  Mixed Autoregressive-Moving Average Processes 77

    3.4.1 Stationarity and Invertibility Properties, 77

    3.4.2 Autocorrelation Function and Spectrum of M&ed

         Processes, 78

    3.4.3 First-Order Autoregressive-First-Order Moving

         Average Process, 80

    3.4.4 Summary, 83

……