thuml/Anomaly-Transformer

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1038 stars · 272 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of the Anomaly Transformer model from the ICLR 2022 Spotlight paper on unsupervised time series anomaly detection. It uses an Anomaly-Attention mechanism and an association discrepancy criterion with a minimax training strategy.

Use cases

• detect anomalies in time series data
• reproduce ICLR 2022 anomaly detection benchmark results
• run unsupervised anomaly detection on SMD, MSL, SMAP, or PSM datasets
• compare against baseline time series anomaly detection models
• experiment with transformer-based anomaly detection architectures

When to choose

• you need unsupervised anomaly point detection in time series
• you want to reproduce or build on the Anomaly Transformer paper
• you need a strong transformer baseline for time series anomaly research

When to avoid

• you need production-ready, actively maintained anomaly detection tooling
• you want streaming or real-time anomaly detection out of the box
• your data is not time series

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning time-series artificial-intelligence python anomaly-detection time-series transformer research-code iclr-2022

Member repositories

For agents

markdown · JSON · MCP: product_card(name="thuml/Anomaly-Transformer")