Praise for the First Edition "...[t]he book is great for readers who need to applythe methods and models presented but have little background inmathematics and statistics." -MAA Reviews Thoroughly updated throughout, Introduction to Time SeriesAnalysis and Forecasting, Second Edition presents theunderlying theories of time series analysis that are needed toanalyze time-oriented data and construct real-world short- tomedium-term statistical forecasts. Authored by highly-experienced academics and professionals inengineering statistics, the Second Edition featuresdiscussions on both popular and modern time series methodologies aswell as an introduction to Bayesian methods in forecasting.Introduction to Time Series Analysis and Forecasting, SecondEdition also includes: * Over 300 exercises from diverse disciplines including healthcare, environmental studies, engineering, and finance * More than 50 programming algorithms using JMP®, SAS®,and R that illustrate the theory and practicality of forecastingtechniques in the context of time-oriented data * New material on frequency domain and spatial temporaldata analysis * Expanded coverage of the variogram and spectrum withapplications as well as transfer and intervention modelfunctions * A supplementary website featuring PowerPoint®slides, data sets, and select solutions to the problems Introduction to Time Series Analysis and Forecasting, SecondEdition is an ideal textbook upper-undergraduate andgraduate-levels courses in forecasting and time series. The book isalso an excellent reference for practitioners and researchers whoneed to model and analyze time series data to generate forecasts.
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.
Texte du rabat
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:
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.
Contenu
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 on Individual Regression Coefficients 130
3.3.4 Confidence Intervals on the Mean Response 131
3.4 Prediction of New Observations 134
3.5 Model Adequacy Checking 136
3.5.1 Residual Plots 136
3.5.2 Scaled Residuals and PRESS 139
3.5.3 Measures of Leverage and Influence 144
3.6 Variable Selection Methods in Regression 146
3.7 Generalized and Weighted Least Squares 152
3.7.1 Generalized Least Squares 153
3.7.2 Weighted Least Squares 156
3.7.3 Discounted Least Squares 161
3.8 Regression Models for General Time Series Data 177
3.8.1 Detecting Autocorrelation: The DurbinWatson Test 178
3.8.2 Estimating the Parameters in Time Series Regression Models 184
3.9 Econometric Models 205
3.10 R Commands for Chapter 3 209
Exercises 219
4 Exponential Smoothing Methods 233
4.1 Introduction 233
4.2 First-Order Exponential Smoothing 239
4.2.1 The Initial Value, y0 241
4.2.2 The Value of 𝜆 241
4.3 Modeling Time Series Data 245
4.4 Second-Order Exponential Smoothing 247
4.5 Higher-Order Exponential Smoothing 257
4.6 Forecasting 259
4.6.1 Constant Process 259
4.6.2 Linear Trend Process 264
4.6.3 Estimation of 𝜎^{2}_{e} 273
4.6.4 Adaptive Updating of the Discount Factor 274
4.6.5 Model Assessment 276
4.7 Exponential Smoothing for Seasonal Data 277
4.7.1 Additive Seasonal Model 277
4.7.2 Multiplicative Seasonal Model 280
4.8 Exponential Smoothing of Biosurveillance Data 286
4.9 Exponential Smoothers and Arima Models 299
4.10 R Commands for Chapter 4 300
Exercises 311
5 Autoregressive Integrated Moving Average (Arima) Models 327
5.1 Introduction 327
5.2 Linear Models for Stationary Time Series 328
5.2.1 Stationarity 329
5.2.2 Stationary Time Series 329
5.3 Finite Order Moving Average Processes 333
5.3.1 The First-Order Moving Average Process, MA(1) 334
5.3.2 The Second-Order Moving Average Process, MA(2) 336
5.4 Finite Order Autoregressive Processes 337
5.4.1 First-Order Autoregressive Process, ...