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

Learn by doing with this guide to classical and contemporary machine learning approaches to time series data analysis. With data sets, commented R programs, case studies and quizzes, this is an essential and accessible resource for undergraduate and graduate students in statistics and data science, and researchers in data-rich disciplines.

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