  Mechanical devices such as engines, vehicles, aircrafts, etc., are typically
instrumented with numerous sensors to capture the behavior and health of the
machine. However, there are often external factors or variables which are not
captured by sensors leading to time-series which are inherently unpredictable.
For instance, manual controls and/or unmonitored environmental conditions or
load may lead to inherently unpredictable time-series. Detecting anomalies in
such scenarios becomes challenging using standard approaches based on
mathematical models that rely on stationarity, or prediction models that
utilize prediction errors to detect anomalies. We propose a Long Short Term
Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD)
that learns to reconstruct 'normal' time-series behavior, and thereafter uses
reconstruction error to detect anomalies. We experiment with three publicly
available quasi predictable time-series datasets: power demand, space shuttle,
and ECG, and two real-world engine datasets with both predictive and
unpredictable behavior. We show that EncDec-AD is robust and can detect
anomalies from predictable, unpredictable, periodic, aperiodic, and
quasi-periodic time-series. Further, we show that EncDec-AD is able to detect
anomalies from short time-series (length as small as 30) as well as long
time-series (length as large as 500).
