Introduction to Time Series Analysis and Forecasting

Praise for the First Edition

"...[t]he book is great for readers who need to apply
the methods and models presented but have little background in
mathematics and statistics." -MAA Reviews

Thoroughly updated throughout, Introduction to Time Series
Analysis and Forecasting, Second Edition presents the
underlying theories of time series analysis that are needed to
analyze time-oriented data and construct real-world short- to
medium-term statistical forecasts.

Authored by highly-experienced academics and professionals in
engineering statistics, the Second Edition features
discussions on both popular and modern time series methodologies as
well as an introduction to Bayesian methods in forecasting.
Introduction to Time Series Analysis and Forecasting, Second
Edition also includes:

• Over 300 exercises from diverse disciplines including health
  care, environmental studies, engineering, and finance

• More than 50 programming algorithms using JMP®, SAS®,
  and R that illustrate the theory and practicality of forecasting
  techniques in the context of time-oriented data

• New material on frequency domain and spatial temporal
  data analysis

• Expanded coverage of the variogram and spectrum with
  applications as well as transfer and intervention model
  functions

• A supplementary website featuring PowerPoint®
  slides, data sets, and select solutions to the problems

Introduction to Time Series Analysis and Forecasting, Second
Edition is an ideal textbook upper-undergraduate and
graduate-levels courses in forecasting and time series. The book is
also an excellent reference for practitioners and researchers who
need to model and analyze time series data to generate forecasts.

Autorentext

DOUGLAS C. MONTGOMERY, PhD, is Regents' Professor and ASU Foundation Professor of Engineering at Arizona State University. With over 35 years of academic and consulting experience, Dr. Montgomery has authored or coauthored over 250 journal articles and 13 books. His research interests include design and analysis of experiments, statistical methods for process monitoring and optimization, and the analysis of time-oriented data. CHERYL L. JENNINGS, PhD, is Faculty Associate at Arizona State University. With more than 30 years of experience in the automotive, semiconductor, and banking industries, Dr. Jennings has coauthored two books. Her areas of professional interest include Six Sigma, modeling and analysis, performance management, and process control and improvement. MURAT KULAHCI, PhD, is Associate Professor of Statistics at the Technical University of Denmark and Guest Deputy Professor at the Luleå University of Technology in Sweden. He is the author and/or coauthor of over 60 journal articles and two books. Dr. Kulahci's research interests include time series analysis, design of experiments, and statistical process control and monitoring.

Klappentext
Praise for the First Edition "...[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics." MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts. Authored by highly experienced academics and professionals in engineering statistics, the Second Edition features discussions on both popular and modern time series methodologies as well as an introduction to Bayesian methods in forecasting. Introduction to Time Series Analysis and Forecasting, Second Edition also includes:

• Over 300 exercises from diverse disciplines including health care, environmental studies, engineering, and finance
• More than 50 programming algorithms using JMP®, SAS®, and R that illustrate the theory and practicality of forecasting techniques in the context of time-oriented data
• New material on frequency domain and spatial-temporal data analysis
• Expanded coverage of the variogram and spectrum with applications as well as transfer and intervention model functions
• A supplementary website featuring PowerPoint® slides, data sets, and select solutions to the problems Introduction to Time Series Analysis and Forecasting, Second Edition is an ideal textbook for upper-undergraduate and graduate-level courses in forecasting and time series. The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.

Zusammenfassung
**Praise for the *First Edition

• "[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics." -MAA Reviews

Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts.

Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both popular and modern time series methodologies as well as an introduction to Bayesian methods in forecasting. Introduction to Time Series Analysis and Forecasting, Second Edition also includes:

• Over 300 exercises from diverse disciplines including health care, environmental studies, engineering, and finance
• More than 50 programming algorithms using JMP®, SAS®, and R that illustrate the theory and practicality of forecasting techniques in the context of time-oriented data
• New material on frequency domain and spatial temporal data analysis
• Expanded coverage of the variogram and spectrum with applications as well as transfer and intervention model functions
• A supplementary website featuring PowerPoint® slides, data sets, and select solutions to the problems
  Introduction to Time Series Analysis and Forecasting, Second Edition is an ideal textbook upper-undergraduate and graduate-levels courses in forecasting and time series. The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.

Inhalt

preface xi

1 Introduction to Forecasting 1

1.1 The Nature and Uses of Forecasts 1

1.2 Some Examples of Time Series 6

1.3 The Forecasting Process 13

1.4 Data for Forecasting 16

1.4.1 The Data Warehouse 16

1.4.2 Data Cleaning 18

1.4.3 Imputation 18

1.5 Resources for Forecasting 19

Exercises 20

2 Statistics Background for Forecasting 25

2.1 Introduction 25

2.2 Graphical Displays 26

2.2.1 Time Series Plots 26

2.2.2 Plotting Smoothed Data 30

2.3 Numerical Description of Time Series Data 33

2.3.1 Stationary Time Series 33

2.3.2 Autocovariance and Autocorrelation Functions 36

2.3.3 The Variogram 42

2.4 Use of Data Transformations and Adjustments 46

2.4.1 Transformations 46

2.4.2 Trend and Seasonal Adjustments 48

2.5 General Approach to Time Series Modeling and Forecasting 61

2.6 Evaluating and Monitoring Forecasting Model Performance 64

2.6.1 Forecasting Model Evaluation 64

2.6.2 Choosing Between Competing Models 74

2.6.3 Monitoring a Forecasting Model 77

2.7 R Commands for Chapter 2 84

Exercises 96

3 Regression Analysis and Forecasting 107

3.1 Introduction 107

3.2 Least Squares Estimation in Linear Regression Models 110

3.3 Statistical Inference in Linear Regression 119

3.3.1 Test for Significance of Regression 120

3.3.2 Tests on Individual Regression Coefficients and Groups of Coefficients 123

3.3.3 Confidence Intervals …