would otherwise be spread over time. To the best of our knowledge, these approaches have, however, never been tested on anomaly detection tasks for time series.

In this paper, we propose to compare, in an unsupervised setup, six time series to image encoding, that have successfully provided improvements on supervised case studies in the literature. These six encodings are the Gramian Angular Field, the Markov Transition Field [@wang2015encoding], the Recurrence Plot [@MARWAN2007237], the Grey Scale Encoding [@xu2019online], the Spectrogram and the Scalogram. The last two, originate from spectral analysis in Signal Processing and are very common tools for signal analysis [@boashash2015time]. The framework, described in Figure \[fig:framework\], consists in first, the time series splitting, second, the encoding of each sub series, and last the monitoring of their aggregated residuals when fed through a convolutional auto-encoder (CAE). We compare the results with a similar framework, taking the raw sub series as input, and using a one-dimensional CNN. We demonstrate that, first, the suggested modifications of the encodings are required to obtain meaningful results, and that second, the encodings all offer an advantage for the detection of anomalous time series. We discuss last the computational requirement of the proposed framework.

The remainder 