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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. Readers will learn to apply these methods in R and interpret the results.
A range of pedagogical features support students throughout the book, 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, videos and solutions to the exercises.
Lecture slides are also available for instructors. 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. "This book is a great introduction to the ideas and methods of time series data analysis. Chapter by chapter, it will show you its most valuable features, like the wealth of real examples as well as practical uses of R and graphical visualization." Vera loudina, Texas State University "Lots of good real-world examples together with the use of R helps a lot as do the nice set of exercises.
In time series it is a tricky balance between overdoing theory or just hand waving and here the author does very well. This would make a lovely course text ! " Gareth Janacek, University of East Anglia "Whether you're an undergraduate or graduate student, are curious about time series methods, are looking for a self-paced book, or a reference guide, this is a must-have." Irina Kukuyeva, Fractional Chief Data Officer