具体描述
A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering.
The Fourth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series as well as their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, modern topics are introduced through the book's new features, which include:
A new chapter on multivariate time series analysis, including a discussion of the challenge that arise with their modeling and an outline of the necessary analytical tools
New coverage of forecasting in the design of feedback and feedforward control schemes
A new chapter on nonlinear and long memory models, which explores additional models for application such as heteroscedastic time series, nonlinear time series models, and models for long memory processes
Coverage of structural component models for the modeling, forecasting, and seasonal adjustment of time series
A review of the maximum likelihood estimation for ARMA models with missing values
Numerous illustrations and detailed appendices supplement the book,while extensive references and discussion questions at the end of each chapter facilitate an in-depth understanding of both time-tested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, Time Series Analysis, Fourth Edition is the upper-undergraduate and graduate levels. this book is also an invaluable reference for applied statisticians, engineers, and financial analysts.
作者简介
George E. P. Box, PHD, is Ronald Aylmer Fisher Professor Emeritus of Statistics at the University of Wisconsin-Madison. He is a Fellow of the American Academy of Arts and Sciences and a recipient of the Samuel S. Wilks Memorial Medal of the American Statistical Association, the Shewhart Medal of the American Society for Quality, and the Guy Medal in Gold of the Royal Statistical Society. Dr. Box is the coauthor of Statistics for Experimenters: Design, Innovation, and Discovery, Second Edition; Response Surfaces, Mixtures, and Ridge Analyses, Second Edition; Evolutionary Operation: A Statistical Method for Process Improvement; Statistical Control: By Monitoring and Feedback Adjustment; and Improving Almost Anything: Ideas and Essays, Revised Edition, all published by Wiley.
The late Gwilym M. Jenkins, PHD, was professor of systems engineering at Lancaster University in the United Kingdom, where he was also founder and managing director of the International Systems Corporation of Lancaster? A Fellow of the Institute of Mathematical Statistics and the Institute of Statisticians, Dr. Jenkins had a prestigious career in both academia and consulting work that included positions at Imperial College London, Stanford University,Princeton University, and the University of Wisconsin-Madison. He was widely known for his work on time series analysis, most notably his groundbreaking work with Dr. Box on the Box-Jenkins models.
The late Gregory CD. Reinsel, PHD, was professor and former chair of the department of Statistics at the University of Wisconsin-Madison. Dr. Reinsel's expertise was focused on time series analysis and its applications in areas as diverse as economics, ecology, engineering, and meteorology. He authored over seventy refereed articles and three books, and was a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics.
目录信息
Preface to the Fourth Edition xxi
Preface to the Third Edition xxiii
1 Introduction 1
1.1 Five Important Practical Problems, 2
1.2 Stochastic and Deterministic Dynamic Mathematical Models, 7
1.3 Basic Ideas in Model Building, 16
Part One Stochastic Models and Their Forecasting 19
2 Autocorrelation Function and Spectrum of Stationary Processes 21
2.1 Autocorrelation Properties of Stationary Models, 21
2.2 Spectral Properties of Stationary Models, 35
3 Linear Stationary Models 47
3.1 General Linear Process, 47
3.2 Autoregressive Processes, 55
3.3 Moving Average Processes, 71
3.4 Mixed Autoregressive–Moving Average Processes, 79
4 Linear Nonstationary Models 93
4.1 Autoregressive Integrated Moving Average Processes, 93
4.2 Three Explicit Forms for The Autoregressive Integrated Moving Average Model, 103
4.3 Integrated Moving Average Processes, 114
5 Forecasting 137
5.1 Minimum Mean Square Error Forecasts and Their Properties, 137
5.2 Calculating and Updating Forecasts, 145
5.3 Forecast Function and Forecast Weights, 152
5.4 Examples of Forecast Functions and Their Updating, 157
5.5 Use of State-Space Model Formulation for Exact Forecasting, 170
5.6 Summary, 177
Part Two Stochastic Model Building 193
6 Model Identification 195
6.1 Objectives of Identification, 195
6.2 Identification Techniques, 196
6.3 Initial Estimates for the Parameters, 213
6.4 Model Multiplicity, 221
7 Model Estimation 231
7.1 Study of the Likelihood and Sum-of-Squares Functions, 231
7.2 Nonlinear Estimation, 255
7.3 Some Estimation Results for Specific Models, 268
7.4 Likelihood Function Based on the State-Space Model, 275
7.5 Unit Roots in Arima Models, 280
7.6 Estimation Using Bayes’s Theorem, 287
8 Model Diagnostic Checking 333
8.1 Checking the Stochastic Model, 333
8.2 Diagnostic Checks Applied to Residuals, 335
8.3 Use of Residuals to Modify the Model, 350
9 Seasonal Models 353
9.1 Parsimonious Models for Seasonal Time Series, 353
9.2 Representation of the Airline Data by a Multiplicative (0, 1, 1) × (0, 1, 1)12 Model, 359
9.3 Some Aspects of More General Seasonal ARIMA Models, 375
9.4 Structural Component Models and Deterministic Seasonal Components, 384
9.5 Regression Models with Time Series Error Terms, 397
10 Nonlinear and Long Memory Models 413
10.1 Autoregressive Conditional Heteroscedastic (ARCH) Models, 413
10.2 Nonlinear Time Series Models, 420
10.3 Long Memory Time Series Processes, 428
Part Three Transfer Function and Multivariate Model Building 437
11 Transfer Function Models 439
11.1 Linear Transfer Function Models, 439
11.2 Discrete Dynamic Models Represented by Difference Equations, 447
11.3 Relation Between Discrete and Continuous Models, 458
12 Identification, Fitting, and Checking of Transfer Function Models 473
12.1 Cross-Correlation Function, 474
12.2 Identification of Transfer Function Models, 481
12.3 Fitting and Checking Transfer Function Models, 492
12.4 Some Examples of Fitting and Checking Transfer Function Models, 501
12.5 Forecasting With Transfer Function Models Using Leading Indicators, 509
12.6 Some Aspects of the Design of Experiments to Estimate Transfer Functions, 519
13 Intervention Analysis Models and Outlier Detection 529
13.1 Intervention Analysis Methods, 529
13.2 Outlier Analysis for Time Series, 536
13.3 Estimation for ARMA Models with Missing Values, 543
14 Multivariate Time Series Analysis 551
14.1 Stationary Multivariate Time Series, 552
14.2 Linear Model Representations for Stationary Multivariate Processes, 556
14.3 Nonstationary Vector Autoregressive–Moving Average Models, 570
14.4 Forecasting for Vector Autoregressive–Moving Average Processes, 573
14.5 State-Space Form of the Vector ARMA Model, 575
14.6 Statistical Analysis of Vector ARMA Models, 578
14.7 Example of Vector ARMA Modeling, 588
Part Four Design of Discrete Control Schemes 597
15 Aspects of Process Control 599
15.1 Process Monitoring and Process Adjustment, 600
15.2 Process Adjustment Using Feedback Control, 604
15.3 Excessive Adjustment Sometimes Required by MMSE Control, 620
15.4 Minimum Cost Control with Fixed Costs of Adjustment and Monitoring, 623
15.5 Feedforward Control, 627
15.6 Monitoring Values of Parameters of Forecasting and Feedback Adjustment Schemes, 642
Part Five Charts and Tables 659
Collection of Tables and Charts 661
Collection of Time Series Used for Examples in the Text and in Exercises 669
References 685
Part Six Exercises and Problems 701
Index 729
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我向几位正在攻读计量经济学硕士的朋友推荐了这本书，他们的反馈出奇地一致：这本书在处理“残差诊断”和“模型检验”的章节上做得极其出色，几乎可以作为标准操作流程（SOP）来使用。特别是关于Ljung-Box检验的改进版本以及如何识别异方差性在时间序列中的具体表现，这部分内容写得非常细致和严谨。作者在解释这些统计检验背后的假设条件时，没有使用过于晦涩的哲学思辨，而是直接将其与数据特征联系起来，使得读者能够清晰地认识到，何时应该选择哪种检验，以及检验失败时意味着什么。这种高度的实用性和对统计严谨性的坚守，使得这本书在同行交流中具有很高的参考价值。然而，对于那些主要关注机器学习或深度学习方法来处理序列数据的读者来说，这本书的后半部分可能会让人感到略微“过时”。它对LSTM、Transformer等现代序列模型着墨不多，内容更多地集中在经典的、基于统计学假设的建模范式上。因此，如果你的目标是前沿的AI驱动的时间序列预测，这本书可能需要配合其他更侧重计算方法的书籍一同阅读。
