numenta/NAB resource

Health v2 · maintenance only

Adoption not part of the score

2103 stars · 873 forks observed · 2026-08-28

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

The Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating anomaly detection algorithms on streaming, real-time time-series data. It includes over 50 labeled real-world and artificial time-series data files, scoring scripts, and a real-time-aware scoring mechanism.

Use cases

• benchmark my anomaly detection algorithm on streaming time series
• find labeled time-series data with anomalies for testing
• compare anomaly detectors with a real-time scoring metric
• evaluate how quickly a detector catches anomalies with few false positives
• run a standard anomaly detection benchmark in python

When to choose

• you need a standardized, labeled benchmark for streaming anomaly detection
• you want comparable scores against published detectors like HTM or Skyline
• you need real-world time-series anomaly data for research

When to avoid

• you need image, text, or log anomaly detection benchmarks
• you want a production anomaly detection service rather than a benchmark
• you need actively developed tooling with frequent updates

Facets

dataset · maturity maintenance

benchmarking machine-learning data-science machine-learning data-science analytics time-series python cross-platform anomaly-detection time-series streaming-data benchmark labeled-data

Member repositories

For agents

markdown · JSON · MCP: product_card(name="numenta/NAB")