mlsanityData to 14 Oct 2026

TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detection

arXiv 2311.18061 ↗First posted 29 Nov 2023cs.LG cs.NEcited by 6

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Abstract (arXiv)

The surge in real-time data collection across various industries has underscored the need for advanced anomaly detection in both univariate and multivariate time series data. This paper introduces TransNAS-TSAD, a framework that synergizes the transformer architecture with neural architecture search (NAS), enhanced through NSGA-II algorithm optimization. This approach effectively tackles the complexities of time series data, balancing computational efficiency with detection accuracy. Our evaluation reveals that TransNAS-TSAD surpasses conventional anomaly detection models due to its tailored architectural adaptability and the efficient exploration of complex search spaces, leading to marked improvements in diverse data scenarios. We also introduce the Efficiency-Accuracy-Complexity Score (EACS) as a new metric for assessing model performance, emphasizing the balance between accuracy and computational resources. TransNAS-TSAD sets a new benchmark in time series anomaly detection, offering a versatile, efficient solution for complex real-world applications. This research highlights the TransNAS-TSAD potential across a wide range of industry applications and paves the way for future developments in the field.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 26 of its 83 references that are in the map. First posted on arXiv 29 Nov 2023.

Builds on

26 of 83 references.

References · 26

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  2. 2021Transformers in Vision: A Surveycited by 1,013
  3. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 607
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14 more
  1. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  2. 2018Auto-Keras: An Efficient Neural Architecture Search Systemcited by 171
  3. 2020A Survey on Evolutionary Neural Architecture Searchcited by 142
  4. 2020NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Searchcited by 76
  5. 2021Anomaly Detection in Dynamic Graphs via Transformercited by 59
  6. 2020MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulationcited by 57
  7. 2021Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signalscited by 41
  8. 2022Deep Learning for Anomaly Detection in Log Data: A Surveycited by 28
  9. 2020Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunitiescited by 27
  10. 2019Multi-Objective Reinforced Evolution in Mobile Neural Architecture Searchcited by 24
  11. 2022Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessmentcited by 17
  12. 2022Multi-Objective Model Selection for Time Series Forecastingcited by 8
  13. 2021Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)cited by 4
  14. 2023An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zonecited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2024UnitNorm: Rethinking Normalization for Transformers in Time Seriescited by 2
  2. 2025Data-Driven Heat Pump Management: Combining Machine Learning with Anomaly Detection for Residential Hot Water Systemscited by 0
  3. 2025A Comprehensive Forecasting-Based Framework for Time Series Anomaly Detection: Benchmarking on the Numenta Anomaly Benchmark (NAB)cited by 0
  4. 2025Neural Architecture Search for global multi-step Forecasting of Energy Production Time Seriescited by 0
  5. 2026Refining Decision Boundaries In Anomaly Detection Using Similarity Search Within the Feature Spacecited by 0
  6. 2026VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detectioncited by 0