Название: Time Series for Data Science: Analysis and Forecasting
Автор: Wayne A. Woodward, Bivin P. Sadler, Stephen D. Robertson
Издательство: CRC Press
Год: 2022
Страниц: 529
Язык: английский
Формат: pdf (true), epub
Размер: 63.8 MB

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.

For software choice, we have created an R package tswge, which is available on CRAN to accompany this book. Extensive discussion of the use of tswge functions is given within the chapters and in appendices following each chapter. The tswge package currently contains about 70 functions and that number will continue to grow. We have added guidance concerning R usage throughout the entire book, including code for nearly all examples and corresponding figures. The CRAN package tswge also contains many datasets, several of them containing real data, including a collection of datasets associated with figures and examples in the book. Because of the clearly described R syntax, students should not be required to have previous knowledge of R. The number of examples and the focus on participation by the reader to enter the provided R code will ground the students with the necessary R coding skills. In fact, this textbook could be a resource for an introductory course in R programming with time series applications.

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.