Introduction to Time Series and Forecasting

2. Auflage 2002, Corr. 9th printing 2010
ISBN: 978-0-387-95351-9
Verlag: Springer

Medienkombination, Englisch, 437 Seiten, Book w. online files/update, Format (B × H): 195 mm x 260 mm, Gewicht: 1146 g

Reihe: Springer Texts in Statistics

This is an introduction to time series that emphasizes methods and analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills. Statisticians and students will learn the latest methods in time series and forecasting, along with modern computational models and algorithms.

Zielgruppe

Graduate

Autoren/Hrsg.

Brockwell, Peter J.

Davis, Richard A.

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik

Inhaltsverzeichnis

Preface
1 INTRODUCTION
1.1 Examples of Time Series
1.2 Objectives of Time Series Analysis
1.3 Some Simple Time Series Models
1.3.3 A General Approach to Time Series Modelling
1.4 Stationary Models and the Autocorrelation Function
1.4.1 The Sample Autocorrelation Function
1.4.2 A Model for the Lake Huron Data
1.5 Estimation and Elimination of Trend and Seasonal Components
1.5.1 Estimation and Elimination of Trend in the Absence of Seasonality
1.5.2 Estimation and Elimination of Both Trend and Seasonality
1.6 Testing the Estimated Noise Sequence
1.7 Problems
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
2.4.2 Estimation of $\gamma(\cdot)$ and $\rho(\cdot)$
2.5 Forecasting Stationary Time Series
2.5.3 Prediction of a Stationary Process in Terms of Infinitely Many Past Values
2.6 The Wold Decomposition
1.7 Problems
3 ARMA MODELS
3.1 ARMA($p,q$) Processes
3.2 The ACF and PACF of an ARMA$(p,q)$ Process
3.2.1 Calculation of the ACVF
3.2.2 The Autocorrelation Function
3.2.3 The Partial Autocorrelation Function
3.3 Forecasting ARMA Processes
1.7 Problems
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
1.7 Problems
5 MODELLING AND PREDICTION WITH ARMA PROCESSES
5.1 Preliminary Estimation
5.1.1 Yule-Walker Estimation
5.1.3 The Innovations Algorithm
5.1.4 The Hannan-Rissanen Algorithm
5.2 Maximum Likelihood Estimation
5.3 Diagnostic Checking
5.3.1 The Graph of $\t=1,\ldots,n\
5.3.2 The Sample ACF of the Residuals