mlsanityData to 14 Oct 2026

SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection

arXiv 2405.11238 ↗First posted 18 May 2024cs.LG cs.AIcited by 6

Not on the map. Why?

Abstract (arXiv)

Despite the prevalence of reconstruction-based deep learning methods, time series anomaly detection remains a tremendous challenge. Existing approaches often struggle with limited temporal contexts, insufficient representation of normal patterns, and flawed evaluation metrics, all of which hinder their effectiveness in detecting anomalous behavior. To address these issues, we introduce a Simple dissimilarity-based approach for time series Anomaly Detection, referred to as SimAD. Specifically, SimAD first incorporates a patching-based feature extractor capable of processing extended temporal windows and employs the EmbedPatch encoder to fully integrate normal behavioral patterns. Second, we design an innovative ContrastFusion module in SimAD, which strengthens the robustness of anomaly detection by highlighting the distributional differences between normal and abnormal data. Third, we introduce two robust enhanced evaluation metrics, Unbiased Affiliation (UAff) and Normalized Affiliation (NAff), designed to overcome the limitations of existing metrics by providing better distinctiveness and semantic clarity. The reliability of these two metrics has been demonstrated by both theoretical and experimental analyses. Experiments conducted on seven diverse time series datasets clearly demonstrate SimAD's superior performance compared to state-of-the-art methods, achieving relative improvements of 19.85% on F1, 4.44% on Aff-F1, 77.79% on NAff-F1, and 9.69% on AUC on six multivariate datasets. Code and pre-trained models are available at https://github.com/EmorZz1G/SimAD.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 32 of its 72 references that are in the map. First posted on arXiv 18 May 2024.

Builds on

32 of 72 references.

References · 32

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  4. 2020Exploring Simple Siamese Representation Learningcited by 2,866
  5. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  6. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  7. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  8. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 632
  9. 2018The UCR Time Series Archivecited by 431
  10. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 410
  11. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  12. 2022A Unified Model for Multi-class Anomaly Detectioncited by 268
20 more
  1. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  2. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
  3. 2023Frequency-domain MLPs are More Effective Learners in Time Series Forecastingcited by 172
  4. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 151
  5. 2023LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasterscited by 129
  6. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  7. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 97
  8. 2022Deep Isolation Forest for Anomaly Detectioncited by 92
  9. 2022Local Evaluation of Time Series Anomaly Detection Algorithmscited by 79
  10. 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
  11. 2021Neural Contextual Anomaly Detection for Time Seriescited by 43
  12. 2022Calibrated One-class Classification for Unsupervised Time Series Anomaly Detectioncited by 40
  13. 2023ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detectioncited by 40
  14. 2023Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Seriescited by 24
  15. 2022Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detectioncited by 18
  16. 2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 18
  17. 2023Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstructioncited by 17
  18. 2023When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detectioncited by 17
  19. 2022TiSAT: Time Series Anomaly Transformercited by 15
  20. 2022Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Datacited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adaptercited by 7
  2. 2025CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detectioncited by 0
  3. 2025Pi-transformer: A prior-informed dual-attention model for multivariate time-series anomaly detectioncited by 0
  4. 2025PathFinder: Advancing Path Loss Prediction for Single-to-Multi-Transmitter Scenariocited by 0
  5. 2026PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detectioncited by 0
  6. 2026Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detectioncited by 0