  Anomaly detection is an active research topic in many different fields such
as intrusion detection, network monitoring, system health monitoring, IoT
healthcare, etc. However, many existing anomaly detection approaches require
either human intervention or domain knowledge, and may suffer from high
computation complexity, consequently hindering their applicability in
real-world scenarios. Therefore, a lightweight and ready-to-go approach that is
able to detect anomalies in real-time is highly sought-after. Such an approach
could be easily and immediately applied to perform time series anomaly
detection on any commodity machine. The approach could provide timely anomaly
alerts and by that enable appropriate countermeasures to be undertaken as early
as possible. With these goals in mind, this paper introduces ReRe, which is a
Real-time Ready-to-go proactive Anomaly Detection algorithm for streaming time
series. ReRe employs two lightweight Long Short-Term Memory (LSTM) models to
predict and jointly determine whether or not an upcoming data point is
anomalous based on short-term historical data points and two long-term
self-adaptive thresholds. Experiments based on real-world time-series datasets
demonstrate the good performance of ReRe in real-time anomaly detection without
requiring human intervention or domain knowledge.
