# An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability

arXiv: https://arxiv.org/abs/2309.01978 · HTML: https://mlsanity.com/paper/2309.01978/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Remaining Useful Life Modelling with an Escalator Health Condition Analytic System](https://mlsanity.com/paper/2306.05436/) (2023)
- [A Change-Point Based Control Chart for Detecting Sparse Changes in High-Dimensional Heteroscedastic Data](https://mlsanity.com/paper/2101.09424/) (2021)

## Built on by (most cited first, 1 of 1)

- [BREAD: Baseline-Referenced Explanations for Anomaly Diagnosis](https://mlsanity.com/paper/2608.10587/) (2026)

## Abstract (arXiv)

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.