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The aim of this thesis is to derive an unsupervised method for detecting anomalies in time series. Autoencoder-based approaches are widely used for the task of detecting anomalies where a model learns to reconstruct the pattern of the given data. The main idea is that the model will be good at reconstructing data that does not contain anomalous behavior. If the model fails to reconstruct an observation it will be marked as anomalous.

In this thesis, the derived method is applied to data from active medical devices manufactured by B. Braun. The given data consist of 6,000 length-varying time series, where the average length is greater than 14,000. Hence, the given sample size is small compared to their lengths. Subsequences of the same pattern where anomalies are expected to appear can be extracted from the time series taking expert knowledge about the data into account. Considering the subsequences for the model training, the problem can betranslated into a problem with a large dataset of short time series.

It is shown that a common autoencoder is able to reconstruct anomalies well and is therefore not useful to solve the task. It is demonstrated that a variational autoencoder works better as there are large differences between the given anomalous observations and their reconstructions. Furthermore, several thresholds for these differences are compared. The relative number of detected anomalies in the two given datasets are 3.12% and 5.03%.