# An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series

arXiv: https://arxiv.org/abs/2201.09172 · HTML: https://mlsanity.com/paper/2201.09172/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 71 references that are in the map.
- First posted on arXiv 23 Jan 2022.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Entity Embeddings of Categorical Variables](https://mlsanity.com/paper/1604.06737/) (2016)
- [Toward Supervised Anomaly Detection](https://mlsanity.com/paper/1401.6424/) (2014)
- [Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data](https://mlsanity.com/paper/1706.03161/) (2017)
- [Tree-based Intelligent Intrusion Detection System in Internet of Vehicles](https://mlsanity.com/paper/1910.08635/) (2019)
- [Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection](https://mlsanity.com/paper/2008.03297/) (2020)
- [Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks](https://mlsanity.com/paper/2011.04121/) (2020)
- [Easy Hyperparameter Search Using Optunity](https://mlsanity.com/paper/1412.1114/) (2014)
- [Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection](https://mlsanity.com/paper/2008.02327/) (2020)
- [A Transfer Learning Framework for Anomaly Detection Using Model of Normality](https://mlsanity.com/paper/2011.06210/) (2020)
- [DNS Typo-squatting Domain Detection: A Data Analytics & Machine Learning Based Approach](https://mlsanity.com/paper/2012.13604/) (2020)
- [Ensemble-based Feature Selection and Classification Model for DNS Typo-squatting Detection](https://mlsanity.com/paper/2006.09272/) (2020)
- [An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments](https://mlsanity.com/paper/2106.15609/) (2021)

## Built on by (most cited first, 9 of 9)

- [Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection](https://mlsanity.com/paper/2307.08390/) (2023)
- [Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions](https://mlsanity.com/paper/2408.03747/) (2024)
- [Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection](https://mlsanity.com/paper/2401.13872/) (2024)
- [An Unsupervised Deep Explainable AI Framework for Localization of Concurrent Replay Attacks in Nuclear Reactor Signals](https://mlsanity.com/paper/2508.09162/) (2025)
- [On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach](https://mlsanity.com/paper/2403.17181/) (2024)
- [PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series](https://mlsanity.com/paper/2411.13951/) (2024)
- [Time Series Based Network Intrusion Detection using MTF-Aided Transformer](https://mlsanity.com/paper/2508.16035/) (2025)
- [Explainable Unsupervised Multi-Anomaly Detection and Temporal Localization in Nuclear Times Series Data with a Dual Attention-Based Autoencoder](https://mlsanity.com/paper/2509.12372/) (2025)
- [Machine Learning-Based Framework for Real Time Detection and Early Prediction of Control Valve Stiction in Industrial Control Systems](https://mlsanity.com/paper/2601.12362/) (2026)

## Abstract (arXiv)

As a substantial amount of multivariate time series data is being produced by the complex systems in Smart Manufacturing, improved anomaly detection frameworks are needed to reduce the operational risks and the monitoring burden placed on the system operators. However, building such frameworks is challenging, as a sufficiently large amount of defective training data is often not available and frameworks are required to capture both the temporal and contextual dependencies across different time steps while being robust to noise. In this paper, we propose an unsupervised Attention-based Convolutional Long Short-Term Memory (ConvLSTM) Autoencoder with Dynamic Thresholding (ACLAE-DT) framework for anomaly detection and diagnosis in multivariate time series. The framework starts by pre-processing and enriching the data, before constructing feature images to characterize the system statuses across different time steps by capturing the inter-correlations between pairs of time series. Afterwards, the constructed feature images are fed into an attention-based ConvLSTM autoencoder, which aims to encode the constructed feature images and capture the temporal behavior, followed by decoding the compressed knowledge representation to reconstruct the feature images input. The reconstruction errors are then computed and subjected to a statistical-based, dynamic thresholding mechanism to detect and diagnose the anomalies. Evaluation results conducted on real-life manufacturing data demonstrate the performance strengths of the proposed approach over state-of-the-art methods under different experimental settings.