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 datasets, 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"--

Sommario

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.

Info autore

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).

Dettagli sul prodotto

• Autori: Juana Sanchez, Juana (University of California Sanchez, Juana (University of California) Sanchez
• Editore: Cambridge University Press Academic
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 30.04.2023
• EAN: 9781108837774
• ISBN: 978-1-108-83777-4
• Pagine: 550
• Categorie: Libri scolastici > Didattica > Formazione professionale; machine learning, COMPUTERS / Information Theory, MATHEMATICS / Probability & Statistics / General, BUSINESS & ECONOMICS / Econometrics, Economic statistics, Probability & statistics, Information theory, Econometrics, Probability and statistics, COMPUTERS / Data Science / Machine Learning, Data analysis: general, Data science and analysis: general, Econometrics and economic statistics