Time Series : A Data Analysis Approach Using R

Robert H Shumway

ISBN 10: 1041031610 / ISBN 13: 9781041031611

Published by CRC Press Feb 2026, 2026

About this Item

Neuware - The goals of this new, second edition of this book are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data. An expanded feature of this edition is the inclusion of many nontrivial data sets illustrating the wealth of potential applications to problems in the biological, physical, and social sciences as well as in economics and medicine.This edition emphasizes a variety of methodological techniques to illustrate solutions to data analysis problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and the analysis of economic and financial problems.Key Features:¿ Presents a balanced and comprehensive treatment of both time and frequency domain methods with an emphasis on data analysis.¿ Detailed R code is included with each numerical example.¿ Includes nontrivial data sets.The book can be used for a one semester/quarter introductory time series course where the prerequisites are an understanding of linear regression, basic calculus-based probability and statistics skills, and math skills at the high-school level. All the numerical examples use the R statistical package without assuming the reader has previously used the software.

Seller Inventory # 9781041031611

Bibliographic Details

• Title: Time Series : A Data Analysis Approach Using R
• Publisher: CRC Press Feb 2026
• Publication Date: 2026
• Language: English
• ISBN 10: 1041031610
• ISBN 13: 9781041031611
• Binding: Buch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 240W x 162H x 22D millimeters 240 millimeters width by 162 millimeters height by 22 millimeters depth
• Weight: 449 grams

About this title

Synopsis

The goals of this new, second edition of this book are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data. An expanded feature of this edition is the inclusion of many nontrivial data sets illustrating the wealth of potential applications to problems in the biological, physical, and social sciences as well as in economics and medicine.

This edition emphasizes a variety of methodological techniques to illustrate solutions to data analysis problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and the analysis of economic and financial problems.

Key Features:

• Presents a balanced and comprehensive treatment of both time and frequency domain methods with an emphasis on data analysis.

• Detailed R code is included with each numerical example.

• Includes nontrivial data sets.

The book can be used for a one semester/quarter introductory time series course where the prerequisites are an understanding of linear regression, basic calculus-based probability and statistics skills, and math skills at the high-school level. All the numerical examples use the R statistical package without assuming the reader has previously used the software.

About the Author

Robert H. Shumway was Professor of Statistics, University of California, Davis. He was a Fellow of the American Statistical Association and won the American Statistical Association Award for Outstanding Statistical Application. He was the author of numerous texts and served on editorial boards such as the Journal of Forecasting and the Journal of the American Statistical Association.

David S. Stoffer is Professor Emeritus of Statistics, University of Pittsburgh. He is a Fellow of the American Statistical Association and has won the American Statistical Association Award for Outstanding Statistical Application. He was on the editorial boards of the Journal of Forecasting, the Annals of Statistical Mathematics, and the Journal of Time Series Analysis. He served as a Program Director in the Division of Mathematical Sciences at the National Science Foundation and as an Associate Editor for the Journal of the American Statistical Association and the Journal of Business & Economic Statistics. The authors have also published the more advanced Time Series Analysis and Its Application: With R Examples, Fifth Edition.