Time Series for Data Scientists
Data Management, Description, Modeling and Forecasting

A user-friendly, introductory, learning-by-doing bridge between classical and machine learning time series analysis with R.

Juana Sanchez (Author)

9781108837774, Cambridge University Press

Hardback, published 11 May 2023

550 pages
25 x 17.5 x 2.6 cm, 1 kg

'This book should be a serious contender if you are looking for an introductory text for an undergraduate course in time series. It is especially suited for a course populated with students having varying degrees of mathematical skill levels. Its conversational approach to introducing time series concepts and the use of insightful examples throughout the book makes it very accessible to students who are not highly trained in abstract mathematical reasoning. Nevertheless, it does not shy away from providing the theoretical underpinnings of various time series models but does so in a manner very accessible to students. The availability of R code throughout the book is an added plus. Even if I am teaching an upper-level graduate course in time series, I would use this book as a supplement simply because of the plethora of examples and data sources it provides.' V. A. Samaranayake, Missouri University of Science and Technology

Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines

Part I. Descriptive Features of Time Series Data: 1. Introduction to time series data
2. Smoothing and decomposing a time series
3. Summary statistics of stationary time series
Part II. Univariate Models of Temporal Dependence: 4. The algebra of differencing and backshifting
5. Stationary stochastic processes
6. ARIMA(p,d,q)(P,D,Q)$_F$ modeling and forecasting
Part III. Multivariate Modeling and Forecasting: 7. Latent process models for time series
8. Vector autoregression
9. Classical regression with ARMA residuals
10. Machine learning methods for time series
References
Index.

Subject Areas: Machine learning [UYQM], Probability & statistics [PBT], Economic statistics [KCHS], Data analysis: general [GPH], Information theory [GPF]