Time Series Analysis: Forecasting and Control, Fourth Edition

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Time Series Analysis: Forecasting and Control, Fourth Edition / George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel

資料種別:

電子ブック

出版情報:

O'Reilly Online Learning: Academic/Public Library Edition
John Wiley & Sons, Inc., 2008

著者名:

ISBN:

9780470272848 [0470272848]
9781118210871 [1118210875]

書誌ID:

OB00614798

子書誌情報

フルテキスト

所蔵情報

書誌詳細

言語:

英語

目次情報:

Preface to the Fourth Edition

Preface to the Third Edition

Introduction / 1：

Five Important Practical Problems / 1.1：

Stochastic and Deterministic Dynamic Mathematical Models / 1.2：

Basic Ideas in Model Building / 1.3：

Stochastic Models and Their Forecasting / Part 1：

Autocorrelation Function and Spectrum of Stationary Processes / 2：

Autocorrelation Properties of Stationary Models / 2.1：

Spectral Properties of Stationary Models / 2.2：

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

Linear Stationary Models / 3：

General Linear Process / 3.1：

Autoregressive Processes / 3.2：

Moving Average Processes / 3.3：

Mixed Autoregressive-Moving Average Processes / 3.4：

Autocovariances, Autocovariance Generating Function, and Stationarity Conditions for a General Linear Process / A3.1：

Recursive Method for Calculating Estimates of Autoregressive Parameters / A3.2：

Linear Nonstationary Models / 4：

Autoregressive Integrated Moving Average Processes / 4.1：

Three Explicit Forms for The Autoregressive Integrated Moving Average Model / 4.2：

Integrated Moving Average Processes / 4.3：

Linear Difference Equations / A4.1：

IMA(0, 1, 1) Process with Deterministic Drift / A4.2：

Arima Processes with Added Noise / A4.3：

Forecasting / 5：

Minimum Mean Square Error Forecasts and Their Properties / 5.1：

Calculating and Updating Forecasts / 5.2：

Forecast Function and Forecast Weights / 5.3：

Examples of Forecast Functions and Their Updating / 5.4：

Use of State-Space Model Formulation for Exact Forecasting / 5.5：

Summary / 5.6：

Correlations Between Forecast Errors / A5.1：

Forecast Weights for Any Lead Time / A5.2：

Forecasting in Terms of the General Integrated Form / A5.3：

Stochastic Model Building / Part 2：

Model Identification / 6：

Objectives of Identification / 6.1：

Identification Techniques / 6.2：

Initial Estimates for the Parameters / 6.3：

Model Multiplicity / 6.4：

Expected Behavior of the Estimated Autocorrelation Function for a Nonstationary Process / A6.1：

General Method for Obtaining Initial Estimates of the Parameters of a Mixed Autoregressive-Moving Average Process / A6.2：

Model Estimation / 7：

Study of the Likelihood and Sum-of-Squares Functions / 7.1：

Nonlinear Estimation / 7.2：

Some Estimation Results for Specific Models / 7.3：

Likelihood Function Based on the State-Space Model / 7.4：

Unit Roots in Arima Models / 7.5：

Estimation Using Bayes's Theorem / 7.6：

Review of Normal Distribution Theory / A7.1：

Review of Linear Least Squares Theory / A7.2：

Exact Likelihood Function for Moving Average and Mixed Processes / A7.3：

Exact Likelihood Function for an Autoregressive Process / A7.4：

Asymptotic Distribution of Estimators for Autoregressive Models / A7.5：

Examples of the Effect of Parameter Estimation Errors on Variances of Forecast Errors and Probability Limits for Forecasts / A7.6：

Special Note on Estimation of Moving Average Parameters / A7.7：

Model Diagnostic Checking / 8：

Checking the Stochastic Model / 8.1：

Diagnostic Checks Applied to Residuals / 8.2：

Use of Residuals to Modify the Model / 8.3：

Seasonal Models / 9：

Parsimonious Models for Seasonal Time Series / 9.1：

Representation of the Airline Data by a Multiplicative (0, 1, 1) x (0, 1, 1)[subscript 12] Model / 9.2：

Some Aspects of More General Seasonal ARIMA Models / 9.3：

Structural Component Models and Deterministic Seasonal Components / 9.4：

Regression Models with Time Series Error Terms / 9.5：

Autocovariances for Some Seasonal Models / A9.1：

Nonlinear and Long Memory Models / 10：

Autoregressive Conditional Heteroscedastic (ARCH) Models / 10.1：

Nonlinear Time Series Models / 10.2：

Long Memory Time Series Processes / 10.3：

Transfer Function and Multivariate Model Building / Part 3：

Transfer Function Models / 11：

Linear Transfer Function Models / 11.1：

Discrete Dynamic Models Represented by Difference Equations / 11.2：

Relation Between Discrete and Continuous Models / 11.3：

Continuous Models with Pulsed Inputs / A11.1：

Nonlinear Transfer Functions and Linearization / A11.2：

Identification, Fitting, and Checking of Transfer Function Models / 12：

Cross-Correlation Function / 12.1：

Identification of Transfer Function Models / 12.2：

Fitting and Checking Transfer Function Models / 12.3：

Some Examples of Fitting and Checking Transfer Function Models / 12.4：

Forecasting With Transfer Function Models Using Leading Indicators / 12.5：

Some Aspects of the Design of Experiments to Estimate Transfer Functions / 12.6：

Use of Cross Spectral Analysis for Transfer Function Model Identification / A12.1：

Choice of Input to Provide Optimal Parameter Estimates / A12.2：

Intervention Analysis Models and Outlier Detection / 13：

Intervention Analysis Methods / 13.1：

Outlier Analysis for Time Series / 13.2：

Estimation for ARMA Models with Missing Values / 13.3：

Multivariate Time Series Analysis / 14：

Stationary Multivariate Time Series / 14.1：

Linear Model Representations for Stationary Multivariate Processes / 14.2：

Nonstationary Vector Autoregressive-Moving Average Models / 14.3：

Forecasting for Vector Autoregressive-Moving Average Processes / 14.4：

State-Space Form of the Vector ARMA Model / 14.5：

Statistical Analysis of Vector ARMA Models / 14.6：

Example of Vector ARMA Modeling / 14.7：

Design of Discrete Control Schemes / Part 4：

Aspects of Process Control / 15：

Process Monitoring and Process Adjustment / 15.1：

Process Adjustment Using Feedback Control / 15.2：

Excessive Adjustment Sometimes Required by MMSE Control / 15.3：

Minimum Cost Control with Fixed Costs of Adjustment and Monitoring / 15.4：

Feedforward Control / 15.5：

Monitoring Values of Parameters of Forecasting and Feedback Adjustment Schemes / 15.6：

Feedback Control Schemes Where the Adjustment Variance is Restricted / A15.1：

Choice of the Sampling Interval / A15.2：

Charts and Tables / Part 5：

Collection of Tables and Charts

Collection of Time Series Used for Examples in the Text and in Exercises

References

Exercises and Problems / Part 6：

Index

Preface to the Fourth Edition

Preface to the Third Edition

Introduction / 1：

Five Important Practical Problems / 1.1：

Stochastic and Deterministic Dynamic Mathematical Models / 1.2：

Basic Ideas in Model Building / 1.3：

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