mlsanityData to 7 Oct 2026

Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

arXiv 1802.04431 ↗First posted 13 Feb 2018cs.LG stat.MLcited by 407

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Abstract (arXiv)

As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity. We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development.

Key facts. Cited by 407 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 29; 2022: 53; 2023: 68; 2024: 86; 2025: 89; 2026: 82.

407 papers.

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  1. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  2. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  3. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 453
  4. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
  5. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  6. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 211
  7. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  8. 2023FITS: Modeling Time Series with 10k Parameterscited by 185
  9. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  10. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
  11. 2024TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysiscited by 113
  12. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
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  1. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  2. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  3. 2022Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusioncited by 96
  4. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 95
  5. 2024UniTS: A Unified Multi-Task Time Series Modelcited by 93
  6. 2022Deep Isolation Forest for Anomaly Detectioncited by 90
  7. 2022Local Evaluation of Time Series Anomaly Detection Algorithmscited by 79
  8. 2024TSLANet: Rethinking Transformers for Time Series Representation Learningcited by 71
  9. 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
  10. 2025TAB: Unified Benchmarking of Time Series Anomaly Detection Methodscited by 58
  11. 2022TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysiscited by 48
  12. 2021Neural Contextual Anomaly Detection for Time Seriescited by 42
  13. 2023A Survey on Deep Learning based Time Series Analysis with Frequency Transformationcited by 40
  14. 2022Calibrated One-class Classification for Unsupervised Time Series Anomaly Detectioncited by 39
  15. 2023ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detectioncited by 38
  16. 2024Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoderscited by 37
  17. 2021Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Versioncited by 35
  18. 2024TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Modelcited by 33
  19. 2021Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filteringcited by 31
  20. 2022PyGOD: A Python Library for Graph Outlier Detectioncited by 31
  21. 2024Large language models can be zero-shot anomaly detectors for time series?cited by 30
  22. 2024Universal Time-Series Representation Learning: A Surveycited by 28
  23. 2023MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detectioncited by 26
  24. 2021Merlion: A Machine Learning Library for Time Seriescited by 25
  25. 2023AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Schemecited by 25
  26. 2024Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learningcited by 25
  27. 2022HyperTime: Implicit Neural Representation for Time Seriescited by 24
  28. 2023Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Seriescited by 24
  29. 2024RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Taskscited by 22
  30. 2024Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspectivecited by 22
  31. 2024TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Modelscited by 22
  32. 2022Detecting Multivariate Time Series Anomalies with Zero Known Labelcited by 21
  33. 2023MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detectioncited by 20
  34. 2024KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networkscited by 20
  35. 2024See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzerscited by 20
  36. 2024Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detectioncited by 19
  37. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  38. 2024MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrastcited by 18

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