Time series analysis is a unique field. It’s a specific kind of analysis that is incredibly helpful for any data occurring over time, but the study of the subject tends to veer toward academic pursuits, graduate studies, or researchers. In no particular order, this article reviews the following books:
- “Time Series Analysis” by James Douglas Hamilton
- “The Analysis of Time Series: An Introduction” by Chris Chatfield
- “Forecasting: Principles and Practice” by Rob J. Hyndman and George Athanasopoulos
- “Introduction to Time Series Analysis and Forecasting” by Douglas C. Montgomery, Cheryl L. Jennings, and Murat Kulahci
- “Practical Time Series Forecasting with R: A Hands-On Guide” by Galit Shmueli and Kenneth C. Lichtendahl Jr.
- “Introductory Time Series with R (Use R!)” by Paul S.P. Cowpertwait and Andrew V. Metcalfe
- “Time Series Analysis: Forecasting and Control” by George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung
Many books fall into two categories: classic texts with the basic theories and fundamentals of time series analysis, and revised editions of academic textbooks with real-world examples and exercises. We picked an array that covers the initial introduction to references and guides along with your time series analysis self-study.
1. “Time Series Analysis” by James Douglas Hamilton
This is an oldie but a goodie. Written in 1994 by James D. Hamilton, a professor of economics at the University of California San Diego, “Time Series Analysis” covers the fundamental concepts and theories of time series analysis.
The book can get a little technical, but it’s a great resource for graduate students or as a reference guide for researchers. It doesn’t cover current applications or real-world data sets, but it’s good for those learning the basics of time series analysis. At almost 800 pages, this title presents the different types of trend analysis, forecasting, theories, recipes, tips, and techniques in detail. Hamilton’s “Time Series Analysis” is the standard introduction and a classic encyclopedia.
2. “The Analysis of Time Series: An Introduction” by Chris Chatfield
“The Analysis of Time Series” also serves as a broad introduction to time series analysis and covers the basics of time series theory and practice. In its sixth edition, Chatfield’s book has remained a staple of data professionals since its first publication, but the editions have been updated over the years to reflect advancements in the field.
The book gives a good overview of time series analysis without being overwhelming. It covers the basics, including methods, forecasting models, systems, and ARIMA probability models that include studying seasonality. It also includes examples and practical advice and comes with a free online appendix.
3. “Forecasting: Principles and Practice” by Rob J. Hyndman and George Athanasopoulos
While most of the books in this list are decades-old staples or textbooks from the past several years, “Forecasting: Principles and Practice” has the distinction of being continuously and recently updated and accessible online. Rob J. Hyndman and George Athanasopoulos feature a free online version of the book through an online textbook publisher website. The print version and Kindle version are available through Amazon but are not as up to date as the online edition.
The two authors provide an introduction to forecasting methods through theory and application. It’s ideal for those wishing to get into forecasting without an in-depth background. The book features real-world data examples from the authors’ own experiences to showcase the information in practice.
4. “Introduction to Time Series Analysis and Forecasting” by Douglas C. Montgomery, Cheryl L. Jennings, and Murat Kulahci
Authors: Douglas C. Montgomery, Cheryl L. Jennings, and Murat Kulahci
“Introduction to Time Series Analysis and Forecasting” is a hands-on textbook that presents the basics of time series analysis and includes data sets to practice statistical forecasting. In addition to covering various methods for forecasting, the book contains over 300 exercises from multiple industries — including finance, healthcare, and engineering. The book also includes over 50 practical programming algorithms to put the concepts to work with time-oriented data.
Like several other titles on this list, this is a solid textbook for graduate studies as well as a handy reference guide for researchers.
5. “Practical Time Series Forecasting with R: A Hands-On Guide” by Galit Shmueli and Kenneth C. Lichtendahl Jr.
Like the title says, “Practical Time Series Forecasting with R” offers a hands-on guide and introduction to time series forecasting. It’s a good textbook for those in graduate studies as well as professional programs or business courses. The book explicitly focuses on the open-source program R and includes practical examples to teach various forecasting methods.
Additionally, it covers popular forecasting methods, forecasting solutions, guided cases with real data sets, and practical approaches. The authors even have a companion website with more learning materials, resources, and data sets.
6. “Introductory Time Series with R (Use R!)” by Paul S.P. Cowpertwait and Andrew V. Metcalfe
This book is a basic introduction to time series and the open-source software R, and is intended for readers who have little to no R knowledge. It gives step-by-step instructions for getting started with time series analysis and how to use R to make it all happen. Each module features practical applications and data to test the analysis. The data sets are also featured on a companion website by the co-author Paul Cowpertwait.
While informative, this is introductory and is intended for new users. It’s ideal for enthusiasts and undergraduate students with a focus on mathematics, economics, business, finance, geography, engineering, or related disciplines.
7. “Time Series Analysis: Forecasting and Control” by George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung
Authors: George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung
“Time Series Analysis: Forecasting and Control” provides the methods and tools for analyzing time series data. The book is currently in its fifth edition and covers modern topics and classical models. It explores key methods for modeling time series, with everything from building to testing to analyzing.
This title includes practical examples and real-world scenarios in fields like finance, economics, and engineering. The fifth edition also includes an expanded chapter of special topics such as unit root testing and specialized models. Like several of the other texts, it focuses on R and includes scripts for model building and forecasting. The book acts as an introductory guide for graduate studies, as well as a practical reference guide for practitioners and researchers in the field.
Some of these books include real-world data sets to begin practicing analysis, forecasting, and uncovering trends. You need to first understand the fundamentals, and then put them into practice—because the best way to learn is by doing. See how real professionals combine R and Tableau to tell time series analysis stories.