Book description
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:
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Regression-based methods, heuristic smoothing methods, and
general time series models
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Basic statistical tools used in analyzing time series data
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Metrics for evaluating forecast errors and methods for
evaluating and tracking forecasting performance over time
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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
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Exponential smoothing techniques for time series with
polynomial components and seasonal data
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Forecasting and prediction interval construction with a
discussion on transfer function models as well as intervention
modeling and analysis
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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.
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.
