twitter/AnomalyDetection

Health v2 · maintenance only

Flags: archived

Adoption not part of the score

3606 stars · 764 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

An R package from Twitter that detects anomalies in time series and numeric vectors using the Seasonal Hybrid ESD (S-H-ESD) algorithm, robust to seasonality and trend. It includes visualization support for exploring detected anomalies.

Use cases

• detect anomalies in system metrics after a software release
• find outliers in user engagement data after an A/B test
• detect anomalies in time series with seasonality and trend
• spot outliers in a vector of numeric values without timestamps
• analyze anomalies in financial or econometric data
• visualize anomalies in long time series like minutely data

When to choose

• you work in R and need statistically robust anomaly detection on seasonal time series
• you need to detect both global and local anomalies in metrics data
• you want built-in visualization of detected anomalies

When to avoid

• you need a maintained library with recent updates and active support
• you need real-time streaming anomaly detection
• you work outside R and don't want an R runtime dependency

Facets

library · maturity maintenance

machine-learning data-science data-visualization data-science analytics time-series python cross-platform r-package anomaly-detection time-series s-h-esd statistics

Member repositories

For agents

markdown · JSON · MCP: product_card(name="twitter/AnomalyDetection")