of Figure \[fig:roc\] and Table \[tab:scores\], we can conclude that for the case study dataset and under the proposed setup, the scalogram is the best performing approach. It achieves the highest scores for the F1 and the AUC metrics.

Furthermore, all proposed modifications for the encodings GAF, MTF, RP and GS improve the performance of the anomaly detection algorithms compared to their original formulations. Most modifications consisted in making the transformations dependent of the whole training set, instead of its sliced input only as originally proposed. This allows to better capture the characteristic behaviour of the main class. For the GS encoding, finding the right $P$ value can have a high impact on the results. Finding this parameter *a priori* is a difficult question. Additionally, not using the conversion to integers also outperforms the baseline. The choice of these parameters is, therefore, sensible in a purely unsupervised task.

Based on the F1-metric comparison, all encodings in their modified versions outperform the baseline. Note that the F1 metric is computed based on the proposed threshold as defined above. The Spectrogram and the Recurrence Plots have a lower AUC than the baseline, which means that with a different setting of the threshold, 