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.
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.
