Time Series Analysis Forecasting and Control.

Time Series Analysis Forecasting and Control.

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Bibliographic Details
Main Author: Box, George E. P.
Format: eBook
Language:English
Published: Newark : John Wiley & Sons, Incorporated, 2015.
Series:New York Academy of Sciences Ser.
Subjects:
Feedback control systems > Mathematical models.
Prediction theory.
Time-series analysis.
Transfer functions.
Electronic books.
Online Access:Click for online access
Table of Contents:
  • Intro
  • Wiley Series in Probability and Statistics
  • Title Page
  • Copyright
  • Dedication
  • Preface to the Fifth Edition
  • Preface to the Fourth Edition
  • Preface to the Third Edition
  • Chapter 1: Introduction
  • 1.1 Five Important Practical Problems
  • 1.2 Stochastic and Deterministic Dynamic Mathematical Models
  • 1.3 Basic Ideas in Model Building
  • Appendix A1.1 Use Of The R Software
  • Exercises
  • Part One: Stochastic Models and Their Forecasting
  • Chapter 2: Autocorrelation Function and Spectrum of Stationary Processes
  • 2.1 Autocorrelation Properties of Stationary Models
  • 2.2 Spectral Properties of Stationary Models
  • Appendix A2.1 Link Between the Sample Spectrum and Autocovariance Function Estimate
  • Exercises
  • Chapter 3: Linear Stationary Models
  • 3.1 General Linear Process
  • 3.2 Autoregressive Processes
  • 3.3 Moving Average Processes
  • 3.4 Mixed Autoregressive-Moving Average Processes
  • Appendix A3.1 Autocovariances, Autocovariance Generating Function, and Stationarity Conditions for a General Linear Process
  • Appendix A3.2 Recursive Method for Calculating Estimates of Autoregressive Parameters
  • Exercises
  • Chapter 4: Linear Nonstationary Models
  • 4.1 Autoregressive Integrated Moving Average Processes
  • 4.2 Three Explicit Forms for the Arima Model
  • 4.3 Integrated Moving Average Processes
  • Appendix A4.1 Linear Difference Equations
  • Appendix A4.2 IMA(0, 1, 1) Process with Deterministic Drift
  • Appendix A4.3 Arima Processes with Added Noise
  • Exercises
  • Chapter 5: Forecasting
  • 5.1 Minimum Mean Square Error Forecasts and Their Properties
  • 5.2 Calculating Forecasts and Probability Limits
  • 5.3 Forecast Function and Forecast Weights
  • 5.4 Examples of Forecast Functions and Their Updating
  • 5.5 Use of State-Space Model Formulation for Exact Forecasting
  • 5.6 Summary
  • Appendix A5.1 Correlation Between Forecast Errors
  • Appendix A5.2 Forecast Weights for Any Lead Time
  • Appendix A5.3 Forecasting in Terms of the General Integrated Form
  • Exercises
  • Part Two: Stochastic Model Building
  • Chapter 6: Model Identification
  • 6.1 Objectives of Identification
  • 6.2 Identification Techniques
  • 6.3 Initial Estimates for the Parameters
  • 6.4 Model Multiplicity
  • Appendix A6.1 Expected Behavior of the Estimated Autocorrelation Function for a Nonstationary Process
  • Exercises
  • Chapter 7: Parameter Estimation
  • 7.1 Study of the Likelihood and Sum-of-Squares Functions
  • 7.2 Nonlinear Estimation
  • 7.3 Some Estimation Results for Specific Models
  • 7.4 Likelihood Function Based on the State-Space Model
  • 7.5 Estimation Using Bayes' Theorem
  • Appendix A7.1 Review of Normal Distribution Theory
  • Appendix A7.2 Review of Linear Least-Squares Theory
  • Appendix A7.3 Exact Likelihood Function for Moving Average and Mixed Processes
  • Appendix A7.4 Exact Likelihood Function for an Autoregressive Process

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