Anomaly detection: research map
84 accepted papers on Anomaly detection in Time series, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 1 clusters and 2 approaches. The busiest year so far is 2026.
Within Time series, its share grew from 3.7% in 2023–24 to 7.5% in 2025–26 (14 → 59 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
Explore Anomaly detection in the interactive map
Working on something in this topic? Describe your idea in scime atlas to see which approach it falls under, the closest papers by meaning and how crowded the spot has become.
Approaches and key papers
anomaly · detection · anomalies · 84 papers
Approaches in this cluster:
- Time series anomaly detection evaluation (47 papers)
Evaluate and improve anomaly detection on time series with metrics, foundation models and LLMs. - Multivariate anomaly detection (37 papers)
Detect anomalies across series using graph dynamics, causality and diffusion reconstruction.
Most cited and most cited since 2024:
- Graph Neural Network-Based Anomaly Detection in Multivariate Time Series (AAAI 2021 · 1,301 citations)
- Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (ICLR 2022 · 242 citations)
- The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark (NeurIPS 2024 · 51 citations)
- When Model Meets New Normals: Test-Time Adaptation for Unsupervised Time-Series Anomaly Detection (AAAI 2024 · 34 citations)