tsfresh

There is also "tsfresh" [1] in the same domain that does «Automatic extraction of 100s of features». It filters the most useful features according to the given task, I quote: «This filtering procedure evaluates the explaining power and importance of each characteristic for the regression or classification tasks at hand.» [1]: https://github.com/blue-yonder/tsfresh

In fact, there are two general approaches to unifying ML with time series: o Unifying ML API and patterns so that time series can be analyzed in the same way as normal tabular data. Example: sktime o Preprocessing libraries applying data transformations to the input time series by converting them to a (tabular) form which can be used by normal ML algorithms. These are typically general or specific libraries for feature engineering, feature learning, feature selection and generic transformations. Examples: https://github.com/blue-yonder/tsfresh or https://github.com/prostodata/prosto Which approach will win is not clear.

Deep feature extraction is important for not only image analysis but also in other areas where specialized tools might be useful such as listed below: o https://github.com/Featuretools/featuretools - Automated feature engineering with main focus on relational structures and deep feature synthesis o https://github.com/blue-yonder/tsfresh - Automatic extraction of relevant features from time series o https://github.com/machinalis/featureforge - creating and testing machine learning features, with a scikit-learn compatible API o https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last! The workflow engine allows for integrating feature training and data wrangling tasks with conventional ML o https://github.com/xiaoganghan/awesome-feature-engineering - other resource related to feature engineering (video

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)

How does featuretools compare to tsfresh: https://github.com/blue-yonder/tsfresh ?