# yahoo/egads

A Java package to automatically detect anomalies in large scale time-series data

Repository: https://github.com/yahoo/egads
Canonical: https://ross.abutalabs.com/products/egads
Language: Java
License: NOASSERTION
License Family: other
Topics: java, time-series, anomaly-detection-models, big-data
Last push: 2023-11-14T22:41:19+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4137, "days_push": 1023, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1189, forks 325 (observed 2026-08-28T04:03:55.447603+00:00)

## What it is
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
- artifact type: library
- maturity: maintenance
- function: machine-learning, analytics, data-science
- domain: time-series, big-data, analytics, monitoring
- platform: jvm, cli
- tags: anomaly-detection, time-series-forecasting, outlier-detection, change-point-detection

## Member repositories
- yahoo/egads (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:55.447603+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:23:22.346535+00:00, confidence not recorded.
  - readme: https://github.com/yahoo/egads (fetched 2026-08-28T04:03:55.447603+00:00, sha 8b29b2d27021)
- Data as of 2026-08-30T08:39:29.467469+00:00.