  During the past decade, many anomaly detection approaches have been
introduced in different fields such as network monitoring, fraud detection, and
intrusion detection. However, they require understanding of data pattern and
often need a long off-line period to build a model or network for the target
data. Providing real-time and proactive anomaly detection for streaming time
series without human intervention and domain knowledge is highly valuable since
it greatly reduces human effort and enables appropriate countermeasures to be
undertaken before a disastrous damage, failure, or other harmful event occurs.
However, this issue has not been well studied yet. To address it, this paper
proposes RePAD, which is a Real-time Proactive Anomaly Detection algorithm for
streaming time series based on Long Short-Term Memory (LSTM). RePAD utilizes
short-term historic data points to predict and determine whether or not the
upcoming data point is a sign that an anomaly is likely to happen in the near
future. By dynamically adjusting the detection threshold over time, RePAD is
able to tolerate minor pattern change in time series and detect anomalies
either proactively or on time. Experiments based on two time series datasets
collected from the Numenta Anomaly Benchmark demonstrate that RePAD is able to
proactively detect anomalies and provide early warnings in real time without
human intervention and domain knowledge.
