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Introduction to time series and forecasting /

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Bibliographic Details
Main Author: Brockwell, Peter J.
Corporate Author: SpringerLink (Online service)
Other Authors: Davis, Richard A.
Format: eBook
Language:English
Published: New York : Springer, [2002]
Edition:2nd ed.
Series:Springer texts in statistics.
Subjects:
Time-series analysis.
Série chronologique.
MATHEMATICS > Probability & Statistics > Time Series.
Tijdreeksen.
Prognoses.
Análise de séries temporais.
Electronic books.
Online Access:Connect to the full text of this electronic book
Connect to the full text of this electronic book
Table of Contents:
  • Cover
  • Table of Contents
  • Preface
  • Chapter 1. Introduction
  • 1.1. Examples of Time Series
  • 1.2. Objectives of Time Series Analysis
  • 1.3. Some Simple Time Series Models
  • 1.4. Stationary Models and the Autocorrelation Function
  • 1.5. Estimation and Elimination of Trend and Seasonal Components
  • 1.6. Testing the Estimated Noise Sequence
  • Problems
  • Chapter 2. Stationary Processes
  • 2.1. Basic Properties
  • 2.2. Linear Processes
  • 2.3. Introduction to ARMA Processes
  • 2.4. Properties of the Sample Mean and Autocorrelation Function
  • 2.5. Forecasting Stationary Time Series
  • 2.6. The Wold Decomposition
  • Problems
  • Chapter 3. ARMA Models
  • 3.1. ARMA(p, q) Processes
  • 3.2. The ACF and PACF of an ARMA(p, q) Process
  • 3.3. Forecasting ARMA Processes
  • Problems
  • Chapter 4. Spectral Analysis
  • 4.1. Spectral Densities
  • 4.2. The Periodogram
  • 4.3. Time-Invariant Linear Filters
  • 4.4. The Spectral Density of an ARMA Process
  • Problems
  • Chapter 5. Modeling and Forecasting with ARMA Processes
  • 5.1. Preliminary Estimation
  • 5.2. Maximum Likelihood Estimation
  • 5.3. Diagnostic Checking
  • 5.4. Forecasting
  • 5.5. Order Selection
  • Problems
  • Chapter 6. Nonstationary and Seasonal Time Series Models
  • 6.1. ARIMA Models for Nonstationary Time Series
  • 6.2. Identification Techniques
  • 6.3. Unit Roots in Time Series Models
  • 6.4. Forecasting ARIMA Models
  • 6.5. Seasonal ARIMA Models
  • 6.6. Regression with ARMA Errors
  • Problems
  • Chapter 7. Multivariate Time Series
  • 7.1. Examples
  • 7.2. Second-Order Properties of Multivariate Time Series
  • 7.3. Estimation of the Mean and Covariance Function
  • 7.4. Multivariate ARMA Processes
  • 7.5. Best Linear Predictors of Second-Order Random Vectors
  • 7.6. Modeling and Forecasting with Multivariate AR Processes
  • 7.7. Cointegration
  • Problems
  • Chapter 8. State-Space Models
  • 8.1. State-Space Representations
  • 8.2. The Basic Structural Model
  • 8.3. State-Space Representation of ARIMA Models
  • 8.4. The Kalman Recursions
  • 8.5. Estimation For State-Space Models
  • 8.6. State-Space Models with Missing Observations
  • 8.7. The EM Algorithm
  • 8.8. Generalized State-Space Models
  • Problems
  • Chapter 9. Forecasting Techniques
  • 9.1. The ARAR Algorithm
  • 9.2. The Holt ... Winters Algorithm
  • 9.3. The Holt ... Winters Seasonal Algorithm
  • 9.4. Choosing a Forecasting Algorithm
  • Problems
  • Chapter 10. Further Topics
  • 10.1. Transfer Function Models
  • 10.2. Intervention Analysis
  • 10.3. Nonlinear Models
  • 10.4. Continuous-Time Models
  • 10.5. Long-Memory Models
  • Problems
  • Appendix A. Random Variables and Probability Distributions
  • A.1. Distribution Functions and Expectation
  • A.2. Random Vectors
  • A.3. The Multivariate Normal Distribution
  • Problems
  • Appendix B. Statistical Complements
  • B.1. Least Squares Estimation
  • B.2. Maximum Likelihood Estimation
  • B.3. Confidence Intervals
  • B.4. Hypothesis Testing
  • Appendix C. Mean Square Convergence
  • C.1. The Cauchy Criterion.