Detecting anomalies in time series data is an important task in areas such as energy, healthcare and security. The progress made in anomaly detection has been mostly based on approaches using supervised machine learning algorithms that require big labelled datasets to be trained. However, in the context of applications, collecting and annotating such large-scale datasets is difficult, time-consuming or even too expensive, while it requires domain knowledge from experts in the field. Therefore, anomaly detection has been such a great challenge for researchers and practitioners. This Thesis proposes a generic, unsupervised and scalable framework for anomaly detection in time series data. The proposed approach is based on a variational autoencoder, a deep generative model that combines variational inference with deep learning. Moreover, the architecture integrates recurrent neural networks to capture the sequential nature of time series data and its temporal dependencies. Furthermore, an attention mechanism is introduced to improve the performance of the encoding-decoding process. The results on solar energy generation and electrocardiogram time series data show the ability of the proposed model to detect anomalous patterns in time series from different fields of application, while providing structured and expressive data representations.
|Qualification||Master of Science|
|Award date||23 Nov 2018|
|Place of Publication||Lisboa|
|Publication status||Published - 2018|