Time Series for Data Science: Analysis and Forecasting (Chapman & Hall/CRC Texts in Statistical Science)
Wayne A. Woodward, Bivin Philip Sadler, Stephen RobertsonData 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 Forecastingdiscusses 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: