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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. 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…
Teaches by example and includes numerous exercises, quizzes and case studies designed to stretch the learner by introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis
Quickly immerses the reader into data and moves from easier to more complex applications
Teaches the reader to apply methods in R and interpret the results. Commented R programs are provided for every example
Connects concepts of time series necessary to do correct classical analysis to how they should be used in contemporary machine learning time series
Extra case studies, videos and solutions to the exercises are available on the companion web site, as well as lecture slides for instructors
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Author
Juana Sanchez,University of California, Los Angeles
Juana Sanchez is Senior Lecturer in Statistics at the University of California, Los Angeles. She is Editor of the Datasets and Stories section of the ASA's Journal of Statistics and Data Science Education and is the author of Probability for Data Scientists (2020).