Enhanced descriptions from Syndetics:

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.

Table of contents provided by Syndetics

• Preface (p. xi)
• 1 Introduction to Forecasting (p. 1)
• 1.1 The Nature and Uses of Forecasts (p. 1)
• 1.2 Some Examples of Time Series (p. 6)
• 1.3 The Forecasting Process (p. 13)
• 1.4 Data for Forecasting (p. 16)
• 1.4.1 The Data Warehouse (p. 16)
• 1.4.2 Data Cleaning (p. 18)
• 1.4.3 Imputation (p. 18)
• 1.5 Resources for Forecasting (p. 19)
• Exercises (p. 20)
• 2 Statistics Background for Forecasting (p. 25)
• 2.1 Introduction (p. 25)
• 2.2 Graphical Displays (p. 26)
• 2.2.1 Time Series Plots (p. 26)
• 2.2.2 Plotting Smoothed Data (p. 30)
• 2.3 Numerical Description of Time Series Data (p. 33)
• 2.3.1 Stationary Time Series (p. 33)
• 2.3.2 Autocovariance and Autocorrelation Functions (p. 36)
• 2.3.3 The Variogram (p. 42)
• 2.4 Use of Data Transformations and Adjustments (p. 46)
• 2.4.1 Transformations (p. 46)
• 2.4.2 Trend and Seasonal Adjustments (p. 48)
• 2.5 General Approach to Time Series Modeling and Forecasting (p. 61)
• 2.6 Evaluating and Monitoring Forecasting Model Performance (p. 64)
• 2.6.1 Forecasting Model Evaluation (p. 64)
• 2.6.2 Choosing Between Competing Models (p. 74)
• 2.6.3 Monitoring a Forecasting Model (p. 77)
• 2.7 R Commands for Chapter 2 (p. 84)
• Exercises (p. 96)
• 3 Regression Analysis and Forecasting (p. 107)
• 3.1 Introduction (p. 107)
• 3.2 Least Squares Estimation in Linear Regression Models (p. 110)
• 3.3 Statistical Inference in Linear Regression (p. 119)
• 3.3.1 Test for Significance of Regression (p. 120)
• 3.3.2 Tests on Individual Regression Coefficients and Groups of Coefficients (p. 123)
• 3.3.3 Confidence Intervals on Individual Regression Coefficients (p. 130)
• 3.3.4 Confidence Intervals on the Mean Response (p. 131)
• 3.4 Prediction of New Observations (p. 134)
• 3.5 Model Adequacy Checking (p. 136)
• 3.5.1 Residual Plots (p. 136)
• 3.5.2 Scaled Residuals and PRESS (p. 139)
• 3.5.3 Measures of Leverage and Influence (p. 144)
• 3.6 Variable Selection Methods in Regression (p. 146)
• 3.7 Generalized and Weighted Least Squares (p. 152)
• 3.7.1 Generalized Least Squares (p. 153)
• 3.7.2 Weighted Least Squares (p. 156)
• 3.7.3 Discounted Least Squares (p. 161)
• 3.8 Regression Models for General Time Series Data (p. 177)
• 3.8.1 Detecting Autocorrelation: The Durbin-Watson Test (p. 178)
• 3.8.2 Estimating the Parameters in Time Series Regression Models (p. 184)
• 3.9 Econometric Models (p. 205)
• 3.10 R Commands for Chapter 3 (p. 209)
• Exercises (p. 219)
• 4 Exponential Smoothing Methods (p. 233)
• 4.1 Introduction (p. 233)
• 4.2 First-Order Exponential Smoothing (p. 239)
• 4.2.1 The Initial Value, ¿ 0 (p. 241)
• 4.2.2 The Value of ¿ (p. 241)
• 4.3 Modeling Time Series Data (p. 245)
• 4.4 Second-Order Exponential Smoothing (p. 247)
• 4.5 Higher-Order Exponential Smoothing (p. 257)
• 4.6 Forecasting (p. 259)
• 4.6.1 Constant Process (p. 259)
• 4.6.2 Linear Trend Process (p. 264)
• 4.6.3 Estimation of ¿ 2 e (p. 273)
• 4.6.4 Adaptive Updating of the Discount Factor (p. 274)
• 4.6.5 Model Assessment (p. 276)
• 4.7 Exponential Smoothing for Seasonal Data (p. 277)
• 4.7.1 Additive Seasonal Model (p. 277)
• 4.7.2 Multiplicative Seasonal Model (p. 280)
• 4.8 Exponential Smoothing of Biosurveillance Data (p. 286)
• 4.9 Exponential Smoothers and Arima Models (p. 299)
• 4.10 R Commands for Chapter 4 (p. 300)
• Exercises (p. 311)
• 5 Autoregressive Integrated Moving Average (Arima) Models (p. 327)
• 5.1 Introduction (p. 327)
• 5.2 Linear Models for Stationary Time Series (p. 328)
• 5.2.1 Stationarity (p. 329)
• 5.2.2 Stationary Time Series (p. 329)
• 5.3 Finite Order Moving Average Processes (p. 333)
• 5.3.1 The First-Order Moving Average Process, MA(1) (p. 334)
• 5.3.2 The Second-Order Moving Average Process, MA(2) (p. 336)
• 5.4 Finite Order Autoregressive Processes (p. 337)
• 5.4.1 First-Order Autoregressive Process, AR(1) (p. 338)
• 5.4.2 Second-Order Autoregressive Process, AR(2) (p. 341)
• 5.4.3 General Autoregressive Process, AR(p) (p. 346)
• 5.4.4 Partial Autocorrelation Function. PACF (p. 348)
• 5.5 Mixed Autoregressive-Moving Average Processes (p. 354)
• 5.5.1 Stationarity of ARMA(p, q) Process (p. 355)
• 5.5.2 Invertibility of ARMA(p, q) Process (p. 355)
• 5.5.3 ACF and PACF of ARMA(p, q) Process (p. 356)
• 5.6 Nonstationary Processes (p. 363)
• 5.6.1 Some Examples of ARIMA(p, d, q) Processes (p. 363)
• 5.7 Time Series Model Building (p. 367)
• 5.7.1 Model Identification (p. 367)
• 5.7.2 Parameter Estimation (p. 368)
• 5.7.3 Diagnostic Checking (p. 368)
• 5.7.4 Examples of Building ARIMA Models (p. 369)
• 5.8 Forecasting Arima Processes (p. 378)
• 5.9 Seasonal Processes (p. 383)
• 5.10 Arima Modeling of Biosurveillance Data (p. 393)
• 5.11 Final Comments (p. 399)
• 5.12 R Commands for Chapter 5 (p. 401)
• Exercises (p. 412)
• 6 Transfer Functions and Intervention Models (p. 427)
• 6.1 Introduction (p. 427)
• 6.2 Transfer Function Models (p. 428)
• 6.3 Transfer Function-Noise Models (p. 436)
• 6.4 Cross-Correlation Function (p. 436)
• 6.5 Model Specification (p. 438)
• 6.6 Forecasting with Transfer Function-Noise Models (p. 456)
• 6.7 Intervention Analysis (p. 462)
• 6.8 R Commands for Chapter 6 (p. 473)
• Exercises (p. 486)
• 7 Survey of Other Forecasting Methods (p. 493)
• 7.1 Multivariate Time Series Models and Forecasting (p. 493)
• 7.1.1 Multivariate Stationary Process (p. 494)
• 7.1.2 Vector ARIMA Models (p. 494)
• 7.1.3 Vector AR (VAR) Models (p. 496)
• 7.2 State Space Models (p. 502)
• 7.3 Arch and Garch Models (p. 507)
• 7.4 Direct Forecasting of Percentiles (p. 512)
• 7.5 Combining Forecasts to Improve Prediction Performance (p. 518)
• 7.6 Aggregation and Disaggregation of Forecasts (p. 522)
• 7.7 Neural Networks and Forecasting (p. 526)
• 7.8 Spectral Analysis (p. 529)
• 7.9 Bayesian Methods in Forecasting (p. 535)
• 7.10 Some Comments on Practical Implementation and Use of Statistical Forecasting Procedures (p. 542)
• 7.11 R Commands for Chapter 7 (p. 545)
• Exercises (p. 550)
• Appendix A Statistical Tables (p. 561)
• Appendix B Data Sets for Exercises (p. 581)
• Appendix C Introduction to R (p. 627)
• Bibliography (p. 631)
• Index (p. 639)

Reviews provided by Syndetics

CHOICE Review

This volume is designed for academic coursework on time series analysis at the advanced undergraduate or first-year graduate level. The reader is assumed to have a background in calculus, probability, and statistics. After a review of some important statistical concepts, authors Montgomery (industrial engineering and statistics, Arizona State Univ.), Jennings (consultant), and Kulahci (informatics and mathematical modeling, Technical Univ. of Denmark) cover three main approaches to time series forecasting: regression, exponential smoothing, and Box-Jenkins ARIMA modeling. The book concludes with a brief chapter introducing more advanced methods. The presentation is focused more on how to apply these forecasting methods rather than on their theoretical development. Many examples and exercises involve the analysis of real data sets. This would be an appropriate resource for use in a first course in time series analysis. It might also be useful as a reference for researchers who want to apply time series analysis to their data sets. Summing Up: Recommended. Upper-division undergraduates, graduate students, researchers, and faculty. B. Borchers New Mexico Institute of Mining and Technology

Author notes provided by Syndetics

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 Lulea 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.