Time Series Analysis With Applications in R

Abstract

The theory and practice of time series analysis have developed rapidly since the appearance 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 accessible 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. All of the plots and numerical output displayed in the book have been produced with the R software, which is available from the R Project for Statistical Computing at www.r-project.org. Some of the numerical output has been edited for additional clarity or for simplicity. R is available as free software under the terms of the Free Software Foundation's GNU General Public License in source code form. It runs on a wide variety of UNIX platforms and similar systems, Windows, and MacOS.