Detecting Anomalies from Streaming Time Series using Matrix Profile and Shapelets Learning

Detecting anomalies in streaming time series data with no prior labels is considered a challenging issue, especially, when anomalies may vary with time. There is a need to deal with time series streams by identifying the anomalous patterns. These patterns can be described by representative features extracted from the data, which expresses abnormal behavior. This work addresses the challenge of performing online and continuous learning over time series data. In this paper, a solution based on the Matrix Profile algorithm and representation learning approach is developed. In light of that, we will show how the integration of these widely used approaches in the streaming context is quite important for learning and detecting anomalies in realtime.