Virtually any random process developing chronologically can be viewed as a time series. In economics, closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroom-tested, Applied Time Series Analysis includes examples across a variety of fields
Table of Contents:
Stationary Time Series. Linear Filters. ARMA Time Series Models. Other Stationary Time Series Models. Nonstationary Time Series Models. Forecasting. Parameter Estimation. Model Identification. Model Building. Vector-Valued (Multivariate) Time Series. Long-Memory Processes. Wavelets. G-Stationary Processes.
About the Author :
Henry L. Gray is a C.F. Frensley Professor Emeritus in the Department of Statistical Science at Southern Methodist University in Dallas, Texas.
Wayne A. Woodward is a professor and chair of the Department of Statistical Science at Southern Methodist University in Dallas, Texas.
Alan C. Elliott is a biostatistician in the Department of Clinical Sciences at the University of Texas Southwestern Medical Center in Dallas.
Review :
"There is scarcely a standard technique that the reader will find left out … this book is highly recommended for those requiring a ready introduction to applicable methods in time series and serves as a useful resource for pedagogical purposes."
—International Statistical Review (2014), 82
"Current time series theory for practice is well summarized in this book."
—Emmanuel Parzen, Texas A&M University
"What an extraordinary range of topics covered, all very insightfully. I like [the authors’] innovations very much, such as the AR factor table."
—David Findley, U.S. Census Bureau (retired)
"… impressive coverage of the scope of time series analysis in both frequency and time domain … One unique feature of the book is the emphasis on factoring the AR polynomial function and its roots. … I commend the authors for having included a number of topics on nonstationary processes … an excellent textbook to adopt for a class and a good introductory book for a student who wants to embark on dissertation research in time series. … the book provides the reader with very good background material to be able to conduct practical and insightful data analysis and be able to comprehend the more theory-oriented books. There are many very good exercises in this book …"
—Hernando Ombao, Journal of the American Statistical Association, March 2013
"The book contains many illustrative examples, theorems with proofs, and applied and theoretical problems at the end of each chapter with real-life applications. Also, the book looks at generating realisations of the mentioned time series models via software packages such as GW-WINKS and R. The book’s material is very valuable and is well presented, so it represents a good reference at both undergraduate and postgraduate levels, and also a good source for all who are interested in time series analysis."
—Hassan S. Bakouch, Journal of Applied Statistics, 2012
