João Pereira

In the age of big data, time series are being generated in massive amounts. In the energy field, smart grids are enabling a unprecedented data acquisition with the integration of sensors and smart devices. In the context of renewable energies, there has been an increasing interest in solar photovoltaic energy generation. These installations are often integrated with smart sensors that measure the energy production. Such amount of data collected makes the quest for developing smart monitoring systems that can detect anomalous behaviour in these systems, trigger alerts and enable maintenance operations. In this paper, we propose a generic, unsupervised and scalable framework for anomaly detection in time series data, based on a variational recurrent autoencoder. Furthermore, we introduce attention in the model, by means of a variational self-attention mechanism (VSAM), to improve the performance of the encoding-decoding process. Afterwards, we perform anomaly detection based on the probabilistic reconstruction scores provided by our model. Our results on solar energy generation time series show the ability of the proposed approach to detect anomalous behaviour in time series data, while providing structured and expressive representations. Since it does not need labels to be trained, our methodology enables new applications for anomaly detection in energy time series data and beyond.

The amount of time series data generated in Healthcare is growing very fast and so is the need for methods that can analyse these data, detect anomalies and provide meaningful insights. However, most of the data available is unlabelled and, therefore, anomaly detection in this scenario has been a great challenge for researchers and practitioners. Recently, unsupervised representation learning with deep generative models has been applied to find representations of data, without the need for big labelled datasets. Motivated by their success, we propose an unsupervised framework for anomaly detection in time series data. In our method, both representation learning and anomaly detection are fully unsupervised. In addition, the training data may contain anomalous data. We first learn representations of time series using a Variational Recurrent Autoencoder. Afterwards, based on those representations, we detect anomalous time series using Clustering and the Wasserstein distance. Our results on the publicly available ECG5000 electrocardiogram dataset show the ability of the proposed approach to detect anomalous heartbeats in a fully unsupervised fashion, while providing structured and expressive data representations. Furthermore, our approach outperforms previous supervised and unsupervised methods on this dataset.