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 howto 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 implemented 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.
About the Author:
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