The book focuses on classical (statistical) methods of forecasting. In this sense, it provides the fundamental notions needed to deal with practical problems. Real world problems are much more complicated and first of all because of the natural of source data which is not limited by univariate numeric data. In most practical cases the success depends on the ability to extract (manually or automatically) good features from heterogeneous data sources. There exist the following frameworks for that purpose:
This is one of those times when R simply outshines Python. Statisticians mostly use R, so they teach with R and write books that use R and create R packages when they develop new methods. It's why it's worth knowing both.
`prophet` is a really good library for time series forecasting. It's especially useful when speed matters where you create an Rshiny forecasting tool. It's not always the best result though. Sometimes a simple exponential smoothing could give a better result too.
o https://github.com/asavinov/lambdo - Combines feature engineering and data mining with strong focus on time series analysis
o https://github.com/blue-yonder/tsfresh - Automatically extract informative features (also from time series)