---
title: outliar
emoji: 🎲
colorFrom: blue
colorTo: gray
sdk: static
pinned: false
license: mit
---

# outliar

An anomaly-detection benchmark on the Numenta Anomaly Benchmark, and an audit of
the metric the field reports.

Pick a detector — including `random`, which is uniform noise and never looks at
the data — and drag the threshold. Point-adjusted F1, the number reported almost
universally in time-series anomaly detection, will tell you the noise is
excellent. The composite score will not.

Everything runs in your browser: the detectors and the four scoring protocols are
a JavaScript port of the NumPy implementation, pinned to it to 1e-9 by a
Python↔Node parity test. NAB itself is fetched from the upstream GitHub raw
endpoint, not redistributed here.

`iforest` is missing from this demo because it needs scikit-learn's fitted trees,
and `random` uses a JavaScript PRNG rather than NumPy's PCG64 — it is still
uniform noise, so the argument is unaffected, but the digits will differ slightly
from the repo's benchmark.

Code, the full 47-series benchmark and the write-up:
**https://github.com/UsmarHaider/outliar**