Unsupervised Deep Learning for Anomaly Detection and Explanation in Sequential Data

With recent successes of recurrent neural networks (RNNs) for machine translation, and handwriting recognition tasks, we hypothesize that RNN approaches might be best suited for unsupervised anomaly detection in time series. In this thesis, we first contribute a comprehensive comparative evaluation of RNN-based deep learning methods for anomaly detection across a wide array of popular deep neural network architectures. In our second major contribution we observe that a key gap of deep learning based anomaly detection methods is the inability to identify portions of the data that led to the detected anomaly. To address this, we propose a novel explainability approach that aims to pinpoint regions of an input that lead to the detected anomaly. In sum, this thesis not only advances the state-of-the-art in deep learning based anomaly detection for time series data but it also contributes novel methods for producing explanations and evaluating explanation quality of anomaly detectors.M.A.S