mlsanityData to 7 Oct 2026

LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

arXiv 1607.00148 ↗First posted 1 Jul 2016cs.AI cs.LG stat.MLcited by 213

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Abstract (arXiv)

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).

Key facts. Cited by 213 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2016.

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Citing papers per year: 2020: 1; 2021: 27; 2022: 46; 2023: 43; 2024: 27; 2025: 37; 2026: 32.

213 papers.

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  1. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
  2. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  3. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 98
  4. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 95
  5. 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
  6. 2022Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Seriescited by 42
  7. 2021Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signalscited by 41
  8. 2022Calibrated One-class Classification for Unsupervised Time Series Anomaly Detectioncited by 39
  9. 2023ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detectioncited by 38
  10. 2021Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Versioncited by 35
  11. 2021Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filteringcited by 31
  12. 2024Large language models can be zero-shot anomaly detectors for time series?cited by 30
38 more
  1. 2023MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detectioncited by 26
  2. 2022Perturbation Learning Based Anomaly Detectioncited by 24
  3. 2023A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directionscited by 22
  4. 2024TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Modelscited by 22
  5. 2022Detecting Multivariate Time Series Anomalies with Zero Known Labelcited by 21
  6. 2024KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networkscited by 20
  7. 2020Sequential Anomaly Detection using Inverse Reinforcement Learningcited by 19
  8. 2022Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Versioncited by 19
  9. 2022Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detectioncited by 18
  10. 2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 17
  11. 2023When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detectioncited by 17
  12. 2022Sintel: A Machine Learning Framework to Extract Insights from Signalscited by 16
  13. 2022TiSAT: Time Series Anomaly Transformercited by 15
  14. 2022Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusioncited by 15
  15. 2023CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detectioncited by 15
  16. 2023Explainable Time Series Anomaly Detection using Masked Latent Generative Modelingcited by 15
  17. 2023Label-Free Multivariate Time Series Anomaly Detectioncited by 14
  18. 2022Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Streamcited by 13
  19. 2022Are we certain it's anomalous?cited by 13
  20. 2023Set Features for Fine-grained Anomaly Detectioncited by 13
  21. 2022Detecting Anomalies within Time Series using Local Neural Transformationscited by 12
  22. 2022MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Seriescited by 12
  23. 2023Unsupervised Deep Learning for IoT Time Seriescited by 11
  24. 2022Time-Series Anomaly Detection with Implicit Neural Representationcited by 10
  25. 2022Deep Contrastive One-Class Time Series Anomaly Detectioncited by 10
  26. 2022A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysiscited by 10
  27. 2023DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systemscited by 10
  28. 2025Harnessing Vision-Language Models for Time Series Anomaly Detectioncited by 9
  29. 2022Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoisingcited by 8
  30. 2023Context-aware Domain Adaptation for Time Series Anomaly Detectioncited by 8
  31. 2023Sparse Binary Transformers for Multivariate Time Series Modelingcited by 8
  32. 2021Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detectioncited by 7
  33. 2021Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learningcited by 7
  34. 2022Unsupervised Network Embedding Beyond Homophilycited by 7
  35. 2023Multivariate Time Series Anomaly Detection via Dynamic Graph Forecastingcited by 7
  36. 2024DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Seriescited by 7
  37. 2025Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomaliescited by 7
  38. 2022Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gitacited by 6

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