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Image for Practical Time Series Analysis for Data Science

Practical Time Series Analysis for Data Science

Robertson, Stephen, Sadler, Bivin Philip, Woodward, Wayne A.

Part of the Chapman & Hall/CRC Texts in Statistical Science Series series

See all formats and editions

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.

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Product Details

Publisher/Imprint Chapman & Hall

ISBN/Ean1000555364 / 9781000555363

FormateBook (EPUB)

Dewey519.55

Published01/08/2022

Book LanguageEnglish

Pages512 pages

Copy LimitsCopy: 30%; print: 30%

NotesDescription based on CIP data; resource not viewed.

BIC GPH Data analysis: general, PBT Probability & statistics