mlsanityData to 7 Oct 2026

DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data

arXiv 2302.10753 ↗First posted 17 Feb 2023cs.LGcited by 3

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Abstract (arXiv)

Anomaly detection techniques enable effective anomaly detection and diagnosis in multi-variate time series data, which are of major significance for today's industrial applications. However, establishing an anomaly detection system that can be rapidly and accurately located is a challenging problem due to the lack of anomaly labels, the high dimensional complexity of the data, memory bottlenecks in actual hardware, and the need for fast reasoning. In this paper, we propose an anomaly detection and diagnosis model, DTAAD, based on Transformer and Dual Temporal Convolutional Network (TCN). Our overall model is an integrated design in which an autoregressive model (AR) combines with an autoencoder (AE) structure. Scaling methods and feedback mechanisms are introduced to improve prediction accuracy and expand correlation differences. Constructed by us, the Dual TCN-Attention Network (DTA) uses only a single layer of Transformer encoder in our baseline experiment, belonging to an ultra-lightweight model. Our extensive experiments on seven public datasets validate that DTAAD exceeds the majority of currently advanced baseline methods in both detection and diagnostic performance. Specifically, DTAAD improved F1 scores by 8.38% and reduced training time by 99% compared to the baseline. The code and training scripts are publicly available on GitHub at https://github.com/Yu-Lingrui/DTAAD.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 31 of its 74 references that are in the map. First posted on arXiv 17 Feb 2023.

Builds on

31 of 74 references.

References · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  5. 2016Layer Normalizationcited by 5,579
  6. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  7. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  8. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  9. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  10. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  11. 2016Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networkscited by 541
  12. 2018The UCR Time Series Archivecited by 421
19 more
  1. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  2. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
  3. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  4. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  5. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 211
  6. 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 181
  7. 2020Multivariate Time-series Anomaly Detection via Graph Attention Networkcited by 165
  8. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  9. 2020Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Casecited by 129
  10. 2018Temporal Pattern Attention for Multivariate Time Series Forecastingcited by 125
  11. 2018Anomaly Detection using One-Class Neural Networkscited by 82
  12. 2018Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detectioncited by 79
  13. 2019Deep Spectral Clustering using Dual Autoencoder Networkcited by 71
  14. 2021Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detectioncited by 42
  15. 2021Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signalscited by 41
  16. 2017Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networkscited by 40
  17. 2021Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encodercited by 39
  18. 2013Dynamic Covariance Models for Multivariate Financial Time Seriescited by 9
  19. 2019Deep Anomaly Detection in Packet Payloadcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directionscited by 6
  2. 2025DQS: A Low-Budget Query Strategy for Enhancing Unsupervised Data-driven Anomaly Detection Approachescited by 0
  3. 2026Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detectioncited by 0