sintel-dev/Orion

Health v2 · maintenance only

Adoption not part of the score

1368 stars · 207 forks observed · 2026-08-28

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

Orion is a Python machine learning library for unsupervised time series anomaly detection, offering verified ML pipelines that flag rare patterns for expert review. It includes benchmarking of methods and a backend database with REST API for building custom anomaly detection platforms.

Use cases

• detect anomalies in time series data without labels
• find unusual patterns in satellite telemetry signals
• benchmark time series anomaly detection methods
• flag anomalous periods in sensor data for expert review
• detect collective anomalies in wind turbine signals
• build a custom time series anomaly detection platform

When to choose

• you need unsupervised anomaly detection on time series with no labeled anomalies
• you want pre-verified ML pipelines instead of hand-tuning models
• you want to benchmark the latest anomaly detection research methods

When to avoid

• you need supervised anomaly detection with labeled training data
• you need real-time streaming anomaly detection at low latency
• you need a production-stable tool - the project is pre-alpha

Facets

library · maturity experimental

machine-learning deep-learning benchmarking data-science machine-learning time-series data-science python anomaly-detection time-series unsupervised-learning generative-adversarial-networks signals automl

Member repositories

For agents

markdown · JSON · MCP: product_card(name="sintel-dev/Orion")