An LSTM-Based Predictive Monitoring Method for Data with Time-varying
  Variability
Jiaqi Qiu Yu Lin Inez Zwetsloot
The recurrent neural network and its variants have shown great success in
processing sequences in recent years. However, this deep neural network has not
aroused much attention in anomaly detection through predictively process
monitoring. Furthermore, the traditional statistic models work on assumptions
and hypothesis tests, while neural network (NN) models do not need that many
assumptions. This flexibility enables NN models to work efficiently on data
with time-varying variability, a common inherent aspect of data in practice.
This paper explores the ability of the recurrent neural network structure to
monitor processes and proposes a control chart based on long short-term memory
(LSTM) prediction intervals for data with time-varying variability. The
simulation studies provide empirical evidence that the proposed model
outperforms other NN-based predictive monitoring methods for mean shift
detection. The proposed method is also applied to time series sensor data,
which confirms that the proposed method is an effective technique for detecting
abnormalities.