their advantage is not certain. Yet, setting this threshold optimally in an unsupervised detection task is an open research question.

At the dataset level, all approaches achieve very low FPR, highlighting that the main class is well learned. Since the networks were designed and trained based on healthy data only, this finding validates their ability to characterise the healthy conditions. The main differences of the different time-series to image encodings are mostly based on their ability to better discriminate the different types of anomalies. The difference in the performance may be due to the fact that some encodings are better suited for some types of anomalies.

While time-series to image encodings provide benefits in terms of performance and partly also in terms of interpretability, using them comes at the expense of an increased computational complexity and required memory. Especially the SC and the MTF, which coincidentally are among the best performing encodings, require a considerable amount of time to encode large numbers of time series. For the MTF, this shortcoming only holds for the bin assignment process which is part of the original formulation. The proposed extension with the SAX-Algorithm ovecomes this limitation.

Discussion
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The results presented above demonstrate 