# Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals

arXiv: https://arxiv.org/abs/2107.12626 · HTML: https://mlsanity.com/paper/2107.12626/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 81 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 29 of 29)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Unsupervised Learning of Video Representations using LSTMs](https://mlsanity.com/paper/1502.04681/) (2015)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks](https://mlsanity.com/paper/1901.04997/) (2019)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series](https://mlsanity.com/paper/1809.04758/) (2018)
- [An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos](https://mlsanity.com/paper/1801.03149/) (2018)
- [Stratified Transfer Learning for Cross-domain Activity Recognition](https://mlsanity.com/paper/1801.00820/) (2017)
- [Spatio-temporal video autoencoder with differentiable memory](https://mlsanity.com/paper/1511.06309/) (2015)
- [Deep Transfer Learning for Cross-domain Activity Recognition](https://mlsanity.com/paper/1807.07963/) (2018)
- [Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks](https://mlsanity.com/paper/1612.00390/) (2016)
- [Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks](https://mlsanity.com/paper/1806.08110/) (2018)
- [Robust PCA for Anomaly Detection in Cyber Networks](https://mlsanity.com/paper/1801.01571/) (2018)
- [Cross-position Activity Recognition with Stratified Transfer Learning](https://mlsanity.com/paper/1806.09776/) (2018)
- [RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process](https://mlsanity.com/paper/1709.02232/) (2017)
- [Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization](https://mlsanity.com/paper/1807.07282/) (2018)
- [Multi-modality Sensor Data Classification with Selective Attention](https://mlsanity.com/paper/1804.05493/) (2018)

## Built on by (most cited first, 30 of 41)

- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization](https://mlsanity.com/paper/2308.14395/) (2023)
- [A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions](https://mlsanity.com/paper/2307.05638/) (2023)
- [Graph Anomaly Detection in Time Series: A Survey](https://mlsanity.com/paper/2302.00058/) (2023)
- [Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey](https://mlsanity.com/paper/2503.13195/) (2025)
- [Anomaly Detection in Aerial Videos with Transformers](https://mlsanity.com/paper/2209.13363/) (2022)
- [Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection](https://mlsanity.com/paper/2308.01639/) (2023)
- [TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detection](https://mlsanity.com/paper/2311.18061/) (2023)
- [Generative Optimization Networks for Memory Efficient Data Generation](https://mlsanity.com/paper/2110.02912/) (2021)
- [Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection](https://mlsanity.com/paper/2110.04352/) (2021)
- [Assessing Domain Gap for Continual Domain Adaptation in Object Detection](https://mlsanity.com/paper/2302.10396/) (2023)
- [DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2302.10753/) (2023)
- [ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context Encoding](https://mlsanity.com/paper/2406.00578/) (2024)
- [CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series](https://mlsanity.com/paper/2505.00415/) (2025)
- [Toward Data Systems That Are Business Semantic Centric and AI Agents Assisted](https://mlsanity.com/paper/2506.05520/) (2025)
- [AI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence](https://mlsanity.com/paper/2508.03436/) (2025)
- [Next-Frame Feature Prediction for Multimodal Deepfake Detection and Temporal Localization](https://mlsanity.com/paper/2511.10212/) (2025)
- [CNTS: Cooperative Network for Time Series](https://mlsanity.com/paper/2302.09800/) (2023)
- [A Marketplace Price Anomaly Detection System at Scale](https://mlsanity.com/paper/2310.04367/) (2023)
- [Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection](https://mlsanity.com/paper/2410.21322/) (2024)
- [Multivariate Time Series Anomaly Detection in Industry 5.0](https://mlsanity.com/paper/2503.15946/) (2025)
- [An LLM-Empowered Low-Resolution Vision System for On-Device Human Behavior Understanding](https://mlsanity.com/paper/2505.01743/) (2025)
- [An Attention-Augmented VAE-BiLSTM Framework for Anomaly Detection in 12-Lead ECG Signals](https://mlsanity.com/paper/2510.05919/) (2025)
- [A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection](https://mlsanity.com/paper/2511.18739/) (2025)
- [Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT](https://mlsanity.com/paper/2601.01701/) (2026)
- [An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2208.09240/) (2022)
- [TSRNet: Simple Framework for Real-time ECG Anomaly Detection with Multimodal Time and Spectrogram Restoration Network](https://mlsanity.com/paper/2312.10187/) (2023)
- [Seamless Monitoring of Stress Levels Leveraging a Universal Model for Time Sequences](https://mlsanity.com/paper/2407.03821/) (2024)
- [Safe Semi-Supervised Contrastive Learning Using In-Distribution Data as Positive Examples](https://mlsanity.com/paper/2408.01872/) (2024)

## Abstract (arXiv)

Nowadays, multi-sensor technologies are applied in many fields, e.g., Health Care (HC), Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can generate a substantial amount of multivariate time-series data. Unsupervised anomaly detection on multi-sensor time-series data has been proven critical in machine learning researches. The key challenge is to discover generalized normal patterns by capturing spatial-temporal correlation in multi-sensor data. Beyond this challenge, the noisy data is often intertwined with the training data, which is likely to mislead the model by making it hard to distinguish between the normal, abnormal, and noisy data. Few of previous researches can jointly address these two challenges. In this paper, we propose a novel deep learning-based anomaly detection algorithm called Deep Convolutional Autoencoding Memory network (CAE-M). We first build a Deep Convolutional Autoencoder to characterize spatial dependence of multi-sensor data with a Maximum Mean Discrepancy (MMD) to better distinguish between the noisy, normal, and abnormal data. Then, we construct a Memory Network consisting of linear (Autoregressive Model) and non-linear predictions (Bidirectional LSTM with Attention) to capture temporal dependence from time-series data. Finally, CAE-M jointly optimizes these two subnetworks. We empirically compare the proposed approach with several state-of-the-art anomaly detection methods on HAR and HC datasets. Experimental results demonstrate that our proposed model outperforms these existing methods.