Time Series for Data Science - Wayne A. Woodward, Bivin Philip Sadler, Stephen Robertson

Time Series for Data Science

Analysis and Forecasting
Buch | Hardcover
528 Seiten
2022
Chapman & Hall/CRC (Verlag)
978-0-367-53794-4 (ISBN)
129,95 inkl. MwSt
Practical Time Series Analysis for Data Science is an accessible guide that doesn’t require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.
Data Science students and practitioners want to find a forecast that “works” and don’t want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.

This book is an accessible guide that doesn’t require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.

Features:



Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models.
Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy.
Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank.
There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.

Wayne Woodward, Bivin Sadler, Stephen Robertson

1. Working with Data Collected Over Time, 2. Exploring Time Series Data, 3. Statistical Basics for Time Series Analysis, 4. The Frequency Domain, 5. ARMA Models, 6. ARMA Fitting and Forecasting, 7. ARIMA, Seasonal,and ARCH/GARCH Models, 8. Time Series Regression, 9. Model Assessment, 10. Multivariate Time Series, 11. Deep Neural Network Based Time Series Models

Erscheinungsdatum
Reihe/Serie Chapman & Hall/CRC Texts in Statistical Science
Zusatzinfo 74 Tables, black and white; 268 Line drawings, black and white; 4 Halftones, black and white; 272 Illustrations, black and white
Sprache englisch
Maße 178 x 254 mm
Gewicht 1240 g
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Mathematik / Informatik Mathematik
ISBN-10 0-367-53794-X / 036753794X
ISBN-13 978-0-367-53794-4 / 9780367537944
Zustand Neuware
Informationen gemäß Produktsicherheitsverordnung (GPSR)
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