mlsanityData to 7 Oct 2026

Unsupervised Deep Learning for IoT Time Series

arXiv 2302.03284 ↗First posted 7 Feb 2023cs.LG eess.SPcited by 11

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Abstract (arXiv)

IoT time series analysis has found numerous applications in a wide variety of areas, ranging from health informatics to network security. Nevertheless, the complex spatial temporal dynamics and high dimensionality of IoT time series make the analysis increasingly challenging. In recent years, the powerful feature extraction and representation learning capabilities of deep learning (DL) have provided an effective means for IoT time series analysis. However, few existing surveys on time series have systematically discussed unsupervised DL-based methods. To fill this void, we investigate unsupervised deep learning for IoT time series, i.e., unsupervised anomaly detection and clustering, under a unified framework. We also discuss the application scenarios, public datasets, existing challenges, and future research directions in this area.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 61 of its 206 references that are in the map. First posted on arXiv 7 Feb 2023.

Builds on

61 of 206 references.

References · 61

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  3. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  4. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  7. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  8. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  9. 2020Self-supervised Learning: Generative or Contrastivecited by 693
  10. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
  11. 2017Generative Adversarial Networks: An Overviewcited by 588
  12. 2018Deep learning for time series classification: a reviewcited by 575
38 more
  1. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  2. 2020Time Series Forecasting With Deep Learning: A Surveycited by 457
  3. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  4. 2018The UCR Time Series Archivecited by 421
  5. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  6. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 329
  7. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  8. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 225
  9. 2020Time Series Data Augmentation for Deep Learning: A Surveycited by 225
  10. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 213
  11. 2020A review on outlier/anomaly detection in time series datacited by 187
  12. 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 181
  13. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  14. 2019Deep Learning for Spatio-Temporal Data Mining: A Surveycited by 171
  15. 2020Multivariate Time-series Anomaly Detection via Graph Attention Networkcited by 165
  16. 2019Time-Series Anomaly Detection Service at Microsoftcited by 163
  17. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  18. 2018Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Datasetcited by 98
  19. 2020On Interpretability of Artificial Neural Networks: A Surveycited by 88
  20. 2016Quasi-Recurrent Neural Networkscited by 87
  21. 2017Dilated Recurrent Neural Networkscited by 83
  22. 2020Privacy in Deep Learning: A Surveycited by 70
  23. 2017Deep Learning for IoT Big Data and Streaming Analytics: A Surveycited by 59
  24. 2020Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approachcited by 56
  25. 2017Deep Learning for Time-Series Analysiscited by 55
  26. 2020Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Artcited by 55
  27. 2018RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Seriescited by 54
  28. 2018A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Securitycited by 42
  29. 2021Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detectioncited by 42
  30. 2019Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learningcited by 40
  31. 2020TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networkscited by 29
  32. 2019Machine Learning in IoT Security: Current Solutions and Future Challengescited by 25
  33. 2020A Benchmark Study on Time Series Clusteringcited by 25
  34. 2018Active Learning for Wireless IoT Intrusion Detectioncited by 19
  35. 2017Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learningcited by 18
  36. 2021Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcarecited by 15
  37. 2017Internet of Things: An Overviewcited by 14
  38. 2022Robust Time Series Denoising with Learnable Wavelet Packet Transformcited by 10

50 most cited of 61 All 61 (JSON)

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Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 7.

11 papers.

  1. 2025FreCT: Frequency-augmented Convolutional Transformer for Robust Time Series Anomaly Detectioncited by 10
  2. 2025Foundation Models for CPS-IoT: Opportunities and Challengescited by 5
  3. 2024Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglementcited by 4
  4. 2025Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learningcited by 4
  5. 2025Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applicationscited by 3
  6. 2025OASIS: Open-world Adaptive Self-supervised and Imbalanced-aware Systemcited by 2
  7. 2023Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detectioncited by 1
  8. 2025From Dense to Sparse: Event Response for Enhanced Residential Load Forecastingcited by 1
  9. 2023Image Transformation for IoT Time-Series Data: A Reviewcited by 0
  10. 2024Unsupervised Reservoir Computing for Multivariate Denoising of Severely Contaminated Signalscited by 0
  11. 2025AWEMixer: Adaptive Wavelet-Enhanced Mixer Network for Long-Term Time Series Forecastingcited by 0