Juana Sanchez

Time Series for Data Scientists (eBook, PDF)

• Format: PDF

Produktbeschreibung

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

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 3. Mai 2023
• Englisch
• ISBN-13: 9781108950176
• Artikelnr.: 68952020

Autorenporträt

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).

Inhaltsangabe

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.