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.
