Time Series for Data Science: Analysis and Forecasting - Chapman & Hall/CRC Texts in Statistical Science Series

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.

ISBN:	9780367537944
Publisher:	Taylor & Francis
Imprint:	Chapman & Hall/CRC
Published date:	01 Aug 2022
DEWEY:	519.55
DEWEY edition:	23
Language:	English
Number of pages:	512
Weight:	1128g
Height:	184mm
Width:	261mm
Spine width:	36mm

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