File size: 1,142 Bytes
11dc2e1 ef95487 11dc2e1 ef95487 11dc2e1 ef95487 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ---
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**
|