Real-Time Anomaly Detection in Time Series Data Streams

Description The research goal of my master thesis (I’ve done in cooperation with trivago) was to find real-time capable solutions to automatically detect anomalies in time series data streams, which are especially useful to monitor servers. I evaluated several algorithms and finally ensembled an own algorithm which meets almost all of the previously gathered requirements. In the figures, the red area indicates an anomalous region. When the algorithm detects an anomaly outside this area, it is a false-positive (should be minimized as much as possible), when the algorithm detects an anomaly inside the red area, it is a true-positive (we wanted to detect this). [Read More]