- a number of smart features (usually a few k) depending on the series (using lags, aggregates, curve fits, combinations of features, ...)
- an iterative algorithm that selects features using maximum relevance (~ correlation with the target) / minimum redundancy and adds them to the model
- simple pca and ridge regression (because it's fast)
- a few optimizations of the final model (removing features, selecting a better ridge regression alpha with CV, ...)
The stack is pure Clojure / Clojurescript.
