Time Series for Data Science - Analysis and Forecasting (Hardcover)

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.

General

Imprint:	Crc Press
Country of origin:	United Kingdom
Series:	Chapman & Hall/CRC Texts in Statistical Science
Release date:	July 2022
First published:	2022
Authors:	Wayne A. Woodward • Bivin Philip Sadler • Stephen Robertson
Dimensions:	254 x 178 x 36mm (L x W x T)
Format:	Hardcover
Pages:	506
ISBN-13:	978-0-367-53794-4
Categories:	Books > Reference & Interdisciplinary > Communication studies > Data analysis Books > Science & Mathematics > Mathematics > Probability & statistics
LSN:	0-367-53794-X
Barcode:	9780367537944

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