StreamAD

Anomaly detection for data streams/time series. Detectors process the univariate or multivariate data one by one to simulte a real-time scene.

Documentation

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Installation

The stable version can be installed from PyPI:

pip install streamad

The stable version can be installed from conda-forge:

conda install --channel "conda-forge" streamad

The development version can be installed from GitHub:

pip install git+https://github.com/Fengrui-Liu/StreamAD

MODELS

For univariate time series

Model Example	API Usage	Paper
KNNCAD	streamad.model.KNNDetector	Conformalized density- and distance-based anomaly detection in time-series data
SPOT	streamad.model.SpotDetector	Anomaly detection in streams with extreme value theory
RRCF	streamad.model.RrcfDetector	Robust random cut forest based anomaly detection on streams
Spectral Residual	streamad.model.SRDetector	Time-series anomaly detection service at microsoft
Z score	streamad.model.ZScoreDetector	Standard score

For multivariate time series, also compatible with univariate time series.

Models Example	API Usage	Paper
xStream	streamad.model.xStramDetector	Xstream: outlier detection in feature-evolving data streams
RShash	streamad.model.RShashDetector	Subspace Outlier Detection in Linear Time with Randomized Hashing
HSTree	streamad.model.HSTreeDetector	Fast Anomaly Detection for Streaming Data

QuickStart

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