  Anomaly detection is the process of identifying unexpected events or
ab-normalities in data, and it has been applied in many different areas such as
system monitoring, fraud detection, healthcare, intrusion detection, etc.
Providing real-time, lightweight, and proactive anomaly detection for time
series with neither human intervention nor domain knowledge could be highly
valuable since it reduces human effort and enables appropriate countermeasures
to be undertaken before a disastrous event occurs. To our knowledge, RePAD
(Real-time Proactive Anomaly Detection algorithm) is a generic approach with
all above-mentioned features. To achieve real-time and lightweight detection,
RePAD utilizes Long Short-Term Memory (LSTM) to detect whether or not each
upcoming data point is anomalous based on short-term historical data points.
However, it is unclear that how different amounts of historical data points
affect the performance of RePAD. Therefore, in this paper, we investigate the
impact of different amounts of historical data on RePAD by introducing a set of
performance metrics that cover novel detection accuracy measures, time
efficiency, readiness, and resource consumption, etc. Empirical experiments
based on real-world time series datasets are conducted to evaluate RePAD in
different scenarios, and the experimental results are presented and discussed.
