Algorithm 1: Supervised Time Series Feature Generation: Repeated iteration over all time series variables and time series representations.

Input: X: Set of $\left|I\right|$ time series with $\left|P\right|$ measuring points und $\left|V\right|$ variables; y:      Vector of labels of the $\left|I\right|$ time series; A: Set of aggregation functions; $f_r$:      Rating function for features; $n_{repeats}$: Number of repetitions (d in [8])