yahoo/egads

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1189 stars · 325 forks observed · 2026-08-28

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

EGADS (Extensible Generic Anomaly Detection System) is an open-source Java library for automatically detecting anomalies, outliers, and change points in large-scale time-series data. It combines time-series forecasting models with anomaly detection models to compute expected values and flag deviations, with only Java as a dependency.

Use cases

• detect anomalies in large scale time-series data
• find outliers in metrics with seasonal and trend components
• detect change points in time-series
• forecast expected values for time-series monitoring
• run automated anomaly detection on CSV metric data
• monitor system metrics for unusual behavior

When to choose

• you need a self-contained Java library for time-series anomaly detection with minimal dependencies
• your data has seasonal, trend, and noise components that need modeling
• you want pluggable time-series and anomaly detection models configurable via command line or config files

When to avoid

• your stack is not JVM-based and you prefer Python-native tooling
• you need actively developed features or frequent updates
• you need real-time streaming anomaly detection rather than batch analysis

Facets

library · maturity maintenance

machine-learning analytics data-science time-series big-data analytics monitoring jvm cli anomaly-detection time-series-forecasting outlier-detection change-point-detection

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