Time Series Analysis: With Applications in R

The theory and practice of time series analysis have developed rapidly since the appe- ance in 1970 of the seminal work of George E. P. Box and Gwilym M. Jenkins, Time Series Analysis: Forecasting and Control, now available in its third edition (1994) with co-author Gregory C. Reinsel. Many books on time series have appeared since then, but some of them give too little practical application, while others give too little theoretical background. This book attempts to present both application and theory at a level acc- sible to a wide variety of students and practitioners. Our approach is to mix application and theory throughout the book as they are naturally needed. The book was developed for a one-semester course usually attended by students in statistics, economics, business, engineering, and quantitative social sciences. Basic applied statistics through multiple linear regression is assumed. Calculus is assumed only to the extent of minimizing sums of squares, but a calculus-based introduction to statistics is necessary for a thorough understanding of some of the theory. However, required facts concerning expectation, variance, covariance, and correlation are reviewed in appendices. Also, conditional expectation properties and minimum mean square error prediction are developed in appendices. Actual time series data drawn from various disciplines are used throughout the book to illustrate the methodology. The book contains additional topics of a more advanced nature that can be selected for inclusion in a course if the instructor so chooses.

Jonathan Cryer is Professor Emeritus, University of Iowa, in the Department of Statistics and Actuarial Science. He is a Fellow of the American Statistical Association and received a Collegiate Teaching Award from the University of Iowa College of Liberal Arts and Sciences. He is the author of Statistics for Business: Data Analysis and Modeling, Second Edition, (with Robert B. Miller), the Minitab Handbook, Fifth Edition, (with Barbara Ryan and Brian Joiner), the Electronic Companion to Statistics (with George Cobb), Electronic Companion to Business Statistics (with George Cobb) and numerous research papers.

Kung-Sik Chan is Professor, University of Iowa, in the Department of Statistics and Actuarial Science. He is a Fellow of the American Statistical Association and the Institute of the Mathematical Statistics, and an Elected Member of the International Statistical Institute. He received a Faculty Scholar Award from the University of Iowa in 1996. He is the author of Chaos: A Statistical Perspective (with Howell Tong) and numerous research papers.