Enhanced descriptions from Syndetics:

An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data.

Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts.

Seven easy-to-follow chapters provide intuitive explanations and in-depth coverage of key forecasting topics, including:

Regression-based methods, heuristic smoothing methods, and general time series models

Basic statistical tools used in analyzing time series data

Metrics for evaluating forecast errors and methods for evaluating and tracking forecasting performance over time

Cross-section and time series regression data, least squares and maximum likelihood model fitting, model adequacy checking, prediction intervals, and weighted and generalized least squares

Exponential smoothing techniques for time series with polynomial components and seasonal data

Forecasting and prediction interval construction with a discussion on transfer function models as well as intervention modeling and analysis

Multivariate time series problems, ARCH and GARCH models, and combinations of forecasts

The ARIMA model approach with a discussion on how to identify and fit these models for non-seasonal and seasonal time series

The intricate role of computer software in successful time series analysis is acknowledged with the use of Minitab, JMP, and SAS software applications, which illustrate how the methods are imple-mented in practice. An extensive FTP site is available for readers to obtain data sets, Microsoft Office PowerPoint slides, and selected answers to problems in the book. Requiring only a basic working knowledge of statistics and complete with exercises at the end of each chapter as well as examples from a wide array of fields, Introduction to Time Series Analysis and Forecasting is an ideal text for forecasting and time series courses at the advanced undergraduate and beginning graduate levels. The book also serves as an indispensable reference for practitioners in business, economics, engineering, statistics, mathematics, and the social, environmental, and life sciences.

Table of contents provided by Syndetics

• Preface (p. ix)
• 1 Introduction to Forecasting (p. 1)
• 1.1 The Nature and Uses of Forecasts (p. 1)
• 1.2 Some Examples of Time Series (p. 5)
• 1.3 The Forecasting Process (p. 12)
• 1.4 Resources for Forecasting (p. 14)
• Exercises (p. 15)
• 2 Statistics Background for Forecasting (p. 18)
• 2.1 Introduction (p. 18)
• 2.2 Graphical Displays (p. 19)
• 2.2.1 Time Series Plots (p. 19)
• 2.2.2 Plotting Smoothed Data (p. 22)
• 2.3 Numerical Description of Time Series Data (p. 25)
• 2.3.1 Stationary Time Series (p. 25)
• 2.3.2 Autocovariance and Autocorrelation Functions (p. 28)
• 2.4 Use of Data Transformations and Adjustments (p. 34)
• 2.4.1 Transformations (p. 34)
• 2.4.2 Trend and Seasonal Adjustments (p. 36)
• 2.5 General Approach to Time Series Modeling and Forecasting (p. 46)
• 2.6 Evaluating and Monitoring Forecasting Model Performance (p. 49)
• 2.6.1 Forecasting Model Evaluation (p. 49)
• 2.6.2 Choosing Between Competing Models (p. 57)
• 2.6.3 Monitoring a Forecasting Model (p. 60)
• Exercises (p. 66)
• 3 Regression Analysis and Forecasting (p. 73)
• 3.1 Introduction (p. 73)
• 3.2 Least Squares Estimation in Linear Regression Models (p. 75)
• 3.3 Statistical Inference in Linear Regression (p. 84)
• 3.3.1 Test for Significance of Regression (p. 84)
• 3.3.2 Tests on Individual Regression Coefficients and Groups of Coefficients (p. 87)
• 3.3.3 Confidence Intervals on Individual Regression Coefficients (p. 93)
• 3.3.4 Confidence Intervals on the Mean Response (p. 94)
• 3.4 Prediction of New Observations (p. 96)
• 3.5 Model Adequacy Checking (p. 98)
• 3.5.1 Residual Plots (p. 98)
• 3.5.2 Scaled Residuals and PRESS (p. 100)
• 3.5.3 Measures of Leverage and Influence (p. 105)
• 3.6 Variable Selection Methods in Regression (p. 106)
• 3.7 Generalized and Weighted Least Squares (p. 111)
• 3.7.1 Generalized Least Squares (p. 112)
• 3.7.2 Weighted Least Squares (p. 114)
• 3.7.3 Discounted Least Squares (p. 119)
• 3.8 Regression Models for General Time Series Data (p. 133)
• 3.8.1 Detecting Autocorrelation: The Durbin-Watson Test (p. 134)
• 3.8.2 Estimating the Parameters in Time Series Regression Models (p. 139)
• Exercises (p. 161)
• 4 Exponential Smoothing Methods (p. 171)
• 4.1 Introduction (p. 171)
• 4.2 First-Order Exponential Smoothing (p. 176)
• 4.2.1 The Initial Value, y[subscript 0] (p. 177)
• 4.2.2 The Value of [lambda] (p. 178)
• 4.3 Modeling Time Series Data (p. 180)
• 4.4 Second-Order Exponential Smoothing (p. 183)
• 4.5 Higher-Order Exponential Smoothing (p. 193)
• 4.6 Forecasting (p. 193)
• 4.6.1 Constant Process (p. 193)
• 4.6.2 Linear Trend Process (p. 198)
• 4.6.3 Estimation of [sigma subscript e superscript 2] (p. 207)
• 4.6.4 Adaptive Updating of the Discount Factor (p. 208)
• 4.6.5 Model Assessment (p. 209)
• 4.7 Exponential Smoothing for Seasonal Data (p. 210)
• 4.7.1 Additive Seasonal Model (p. 210)
• 4.7.2 Multiplicative Seasonal Model (p. 214)
• 4.8 Exponential Smoothers and ARIMA Models (p. 217)
• Exercises (p. 220)
• 5 Autoregressive Integrated Moving Average (ARIMA) Models (p. 231)
• 5.1 Introduction (p. 231)
• 5.2 Linear Models for Stationary Time Series (p. 231)
• 5.2.1 Stationarity (p. 232)
• 5.2.2 Stationary Time Series (p. 233)
• 5.3 Finite Order Moving Average (MA) Processes (p. 235)
• 5.3.1 The First-Order Moving Average Process, MA(1) (p. 236)
• 5.3.2 The Second-Order Moving Average Process, MA(2) (p. 238)
• 5.4 Finite Order Autoregressive Processes (p. 239)
• 5.4.1 First-Order Autoregressive Process, AR(1) (p. 240)
• 5.4.2 Second-Order Autoregressive Process, AR(2) (p. 242)
• 5.4.3 General Autoregressive Process, AR(p) (p. 246)
• 5.4.4 Partial Autocorrelation Function, PACF (p. 248)
• 5.5 Mixed Autoregressive-Moving Average (ARMA) Processes (p. 253)
• 5.6 Nonstationary Processes (p. 256)
• 5.7 Time Series Model Building (p. 265)
• 5.7.1 Model Identification (p. 265)
• 5.7.2 Parameter Estimation (p. 266)
• 5.7.3 Diagnostic Checking (p. 266)
• 5.7.4 Examples of Building ARIMA Models (p. 267)
• 5.8 Forecasting ARIMA Processes (p. 275)
• 5.9 Seasonal Processes (p. 282)
• 5.10 Final Comments (p. 286)
• Exercises (p. 287)
• 6 Transfer Functions and Intervention Models (p. 299)
• 6.1 Introduction (p. 299)
• 6.2 Transfer Function Models (p. 300)
• 6.3 Transfer Function-Noise Models (p. 307)
• 6.4 Cross Correlation Function (p. 307)
• 6.5 Model Specification (p. 309)
• 6.6 Forecasting with Transfer Function-Noise Models (p. 322)
• 6.7 Intervention Analysis (p. 330)
• Exercises (p. 338)
• 7 Survey of Other Forecasting Methods (p. 343)
• 7.1 Multivariate Time Series Models and Forecasting (p. 343)
• 7.1.1 Multivariate Stationary Process (p. 343)
• 7.1.2 Vector ARIMA Models (p. 344)
• 7.1.3 Vector AR (VAR) Models (p. 346)
• 7.2 State Space Models (p. 350)
• 7.3 ARCH and GARCH Models (p. 355)
• 7.4 Direct Forecasting of Percentiles (p. 359)
• 7.5 Combining Forecasts to Improve Prediction Performance (p. 365)
• 7.6 Aggregation and Disaggregation of Forecasts (p. 369)
• 7.7 Neural Networks and Forecasting (p. 372)
• 7.8 Some Comments on Practical Implementation and Use of Statistical Forecasting Procedures (p. 375)
• Exercises (p. 378)
• Appendix A Statistical Tables (p. 387)
• Appendix B Data Sets for Exercises (p. 407)
• Bibliography (p. 437)
• Index (p. 443)

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 is Regents' Professor of Industrial Engineering and Statistics at Arizona State University
Cheryl L. Jennings is a Process Design Consultant with Bank of America. An active member of both the American Statistical Association and the American Society for Quality
Murat Kulahci is Associate Professor in Informatics and Mathematical Modelling at the Technical University of Denmark