the validity of the approach since all encodings bring an improvement compared to the baseline of using directly the one dimensional raw time series data as input to the DL algorithm. The results also open interesting points for discussion and perspectives for further developments and further evaluations.

**Defining the transformation:** Using the original formulations of the different encodings [@JMLRpyts] in our case study, led to a poor performance observed in Table \[tab:scores\]. Defining the transformation parameters per input tends to make the encoded images more uniform. While it might be beneficial in a supervised setup to learn the very specific class discriminative features and differences in the images, it is prejudicial in an unsupervised setup, based on the learning of the characteristics of a single (healthy) class. Taking the whole dataset into account when performing the transformation was an essential step in the performance of the proposed encodings.

**Threshold choice:** Learning the right threshold using healthy data only is always a difficult task. In our work, the choice of the 99-th percentile on the residuals seems to provide reasonable results, which are in line with the threshold independent metric AUC. Yet, we highlight the fact that this threshold has been 