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Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning

arXiv 1905.13628 ↗First posted 31 May 2019cs.LG cs.CV stat.MLcited by 40

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Abstract (arXiv)

Time series anomaly detection plays a critical role in automated monitoring systems. Most previous deep learning efforts related to time series anomaly detection were based on recurrent neural networks (RNN). In this paper, we propose a time series segmentation approach based on convolutional neural networks (CNN) for anomaly detection. Moreover, we propose a transfer learning framework that pretrains a model on a large-scale synthetic univariate time series data set and then fine-tunes its weights on small-scale, univariate or multivariate data sets with previously unseen classes of anomalies. For the multivariate case, we introduce a novel network architecture. The approach was tested on multiple synthetic and real data sets successfully.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 May 2019.

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Citing papers per year: 2021: 5; 2022: 9; 2023: 8; 2024: 13; 2025: 2; 2026: 3.

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  1. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
  2. 2023A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directionscited by 22
  3. 2023Deep transfer learning for intrusion detection in industrial control networks: A comprehensive reviewcited by 21
  4. 2021Transfer Learning for Future Wireless Networks: A Comprehensive Surveycited by 18
  5. 2022TiSAT: Time Series Anomaly Transformercited by 15
  6. 2023Unsupervised Deep Learning for IoT Time Seriescited by 11
  7. 2024TimEHR: Image-based Time Series Generation for Electronic Health Recordscited by 8
  8. 2021Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warpingcited by 7
  9. 2023FrAug: Frequency Domain Augmentation for Time Series Forecastingcited by 7
  10. 2022Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features?cited by 6
  11. 2025Text Reinforcement for Multimodal Time Series Forecastingcited by 6
  12. 2023Spacecraft Anomaly Detection with Attention Temporal Convolution Networkcited by 5
28 more
  1. 2024Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series Forecastingcited by 4
  2. 2021Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detectioncited by 3
  3. 2023Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Datacited by 3
  4. 2023Tight Rates in Supervised Outlier Transfer Learningcited by 3
  5. 2024Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Modelscited by 3
  6. 2024ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecastingcited by 3
  7. 2021Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networkscited by 2
  8. 2022Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Datacited by 2
  9. 2024SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approachcited by 2
  10. 2024Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Predictioncited by 2
  11. 2024Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detectioncited by 2
  12. 2021Sequence-to-Sequence Modeling for Action Identification at High Temporal Resolutioncited by 1
  13. 2022Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognitioncited by 1
  14. 2022Don't overfit the history -- Recursive time series data augmentationcited by 1
  15. 2022Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devicescited by 1
  16. 2023EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Seriescited by 1
  17. 2024TreeMIL: A Multi-instance Learning Framework for Time Series Anomaly Detection with Inexact Supervisioncited by 1
  18. 2024Transfer learning applications for anomaly detection in wind turbinescited by 1
  19. 2024Review of Data-centric Time Series Analysis from Sample, Feature, and Periodcited by 1
  20. 2024Time Series Viewmakers for Robust Disruption Predictioncited by 1
  21. 2024F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain Informationcited by 1
  22. 2026Adapt Data to Model: Adaptive Transformation Optimization for Domain-shared Time Series Foundation Modelscited by 1
  23. 2022Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time seriescited by 0
  24. 2022Anomaly Detection for Fraud in Cryptocurrency Time Seriescited by 0
  25. 2024Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysiscited by 0
  26. 2025Time Series Based Network Intrusion Detection using MTF-Aided Transformercited by 0
  27. 2026Foundation CAN LM: A Pretrained Language Model For Automotive CAN Datacited by 0
  28. 2026Sliding-Window Reordering with Overlap Averaging: A Simple Time-Domain Augmentation for Multivariate Forecastingcited by 0