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:

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)