Note: Supplemental materials are not guaranteed with Rental or Used book purchases.
Purchase Benefits
What is included with this book?
Douglas C. Montgomery, PhD, is Regents' Professor of Industrial Engineering and Statistics at Arizona State University. Dr. Montgomery has over thirty years of academic and consulting experience and has devoted his research to engineering statistics, specifically the design and analysis of experiments, statistical methods for process monitoring and optimization, and the analysis of time-oriented data. He has authored or coauthored over 190 journal articles and eleven books, including Introduction to Linear Regression Analysis, Fourth Edition and Generalized Linear Models: With Applications in Engineering and the Sciences, both published by Wiley.
Cheryl L. Jennings, PhD, is a Process Design Consultant with Bank of America. An active member of both the American Statistical Association and the American Society for Quality, her areas of research and professional interest include Six Sigma; modeling and analysis; and process control and improvement. Dr. Jennings earned her PhD in industrial engineering from Arizona State University.
Murat Kulahci, PhD, is Associate Professor in Informatics and Mathematical Modelling at the Technical University of Denmark. He has authored or coauthored over thirty journal articles in the areas of time series analysis, design of experiments, and statistical process control and monitoring.
Preface | p. ix |
Introduction to Forecasting | p. 1 |
The Nature and Uses of Forecasts | p. 1 |
Some Examples of Time Series | p. 5 |
The Forecasting Process | p. 12 |
Resources for Forecasting | p. 14 |
Exercises | p. 15 |
Statistics Background for Forecasting | p. 18 |
Introduction | p. 18 |
Graphical Displays | p. 19 |
Time Series Plots | p. 19 |
Plotting Smoothed Data | p. 22 |
Numerical Description of Time Series Data | p. 25 |
Stationary Time Series | p. 25 |
Autocovariance and Autocorrelation Functions | p. 28 |
Use of Data Transformations and Adjustments | p. 34 |
Transformations | p. 34 |
Trend and Seasonal Adjustments | p. 36 |
General Approach to Time Series Modeling and Forecasting | p. 46 |
Evaluating and Monitoring Forecasting Model Performance | p. 49 |
Forecasting Model Evaluation | p. 49 |
Choosing Between Competing Models | p. 57 |
Monitoring a Forecasting Model | p. 60 |
Exercises | p. 66 |
Regression Analysis and Forecasting | p. 73 |
Introduction | p. 73 |
Least Squares Estimation in Linear Regression Models | p. 75 |
Statistical Inference in Linear Regression | p. 84 |
Test for Significance of Regression | p. 84 |
Tests on Individual Regression Coefficients and Groups of Coefficients | p. 87 |
Confidence Intervals on Individual Regression Coefficients | p. 93 |
Confidence Intervals on the Mean Response | p. 94 |
Prediction of New Observations | p. 96 |
Model Adequacy Checking | p. 98 |
Residual Plots | p. 98 |
Scaled Residuals and PRESS | p. 100 |
Measures of Leverage and Influence | p. 105 |
Variable Selection Methods in Regression | p. 106 |
Generalized and Weighted Least Squares | p. 111 |
Generalized Least Squares | p. 112 |
Weighted Least Squares | p. 114 |
Discounted Least Squares | p. 119 |
Regression Models for General Time Series Data | p. 133 |
Detecting Autocorrelation: The Durbin-Watson Test | p. 134 |
Estimating the Parameters in Time Series Regression Models | p. 139 |
Exercises | p. 161 |
Exponential Smoothing Methods | p. 171 |
Introduction | p. 171 |
First-Order Exponential Smoothing | p. 176 |
The Initial Value, y[subscript 0] | p. 177 |
The Value of [lambda] | p. 178 |
Modeling Time Series Data | p. 180 |
Second-Order Exponential Smoothing | p. 183 |
Higher-Order Exponential Smoothing | p. 193 |
Forecasting | p. 193 |
Constant Process | p. 193 |
Linear Trend Process | p. 198 |
Estimation of [sigma subscript e superscript 2] | p. 207 |
Adaptive Updating of the Discount Factor | p. 208 |
Model Assessment | p. 209 |
Exponential Smoothing for Seasonal Data | p. 210 |
Additive Seasonal Model | p. 210 |
Multiplicative Seasonal Model | p. 214 |
Exponential Smoothers and ARIMA Models | p. 217 |
Exercises | p. 220 |
Autoregressive Integrated Moving Average (ARIMA) Models | p. 231 |
Introduction | p. 231 |
Linear Models for Stationary Time Series | p. 231 |
Stationarity | p. 232 |
Stationary Time Series | p. 233 |
Finite Order Moving Average (MA) Processes | p. 235 |
The First-Order Moving Average Process, MA(1) | p. 236 |
The Second-Order Moving Average Process, MA(2) | p. 238 |
Finite Order Autoregressive Processes | p. 239 |
First-Order Autoregressive Process, AR(1) | p. 240 |
Second-Order Autoregressive Process, AR(2) | p. 242 |
General Autoregressive Process, AR(p) | p. 246 |
Partial Autocorrelation Function, PACF | p. 248 |
Mixed Autoregressive-Moving Average (ARMA) Processes | p. 253 |
Nonstationary Processes | p. 256 |
Time Series Model Building | p. 265 |
Model Identification | p. 265 |
Parameter Estimation | p. 266 |
Diagnostic Checking | p. 266 |
Examples of Building ARIMA Models | p. 267 |
Forecasting ARIMA Processes | p. 275 |
Seasonal Processes | p. 282 |
Final Comments | p. 286 |
Exercises | p. 287 |
Transfer Functions and Intervention Models | p. 299 |
Introduction | p. 299 |
Transfer Function Models | p. 300 |
Transfer Function-Noise Models | p. 307 |
Cross Correlation Function | p. 307 |
Model Specification | p. 309 |
Forecasting with Transfer Function-Noise Models | p. 322 |
Intervention Analysis | p. 330 |
Exercises | p. 338 |
Survey of Other Forecasting Methods | p. 343 |
Multivariate Time Series Models and Forecasting | p. 343 |
Multivariate Stationary Process | p. 343 |
Vector ARIMA Models | p. 344 |
Vector AR (VAR) Models | p. 346 |
State Space Models | p. 350 |
ARCH and GARCH Models | p. 355 |
Direct Forecasting of Percentiles | p. 359 |
Combining Forecasts to Improve Prediction Performance | p. 365 |
Aggregation and Disaggregation of Forecasts | p. 369 |
Neural Networks and Forecasting | p. 372 |
Some Comments on Practical Implementation and Use of Statistical Forecasting Procedures | p. 375 |
Exercises | p. 378 |
Statistical Tables | p. 387 |
Data Sets for Exercises | p. 407 |
Bibliography | p. 437 |
Index | p. 443 |
Table of Contents provided by Ingram. All Rights Reserved. |
The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.