StreamAD#
Anomaly detection for data streams/time series. Detectors process the univariate or multivariate data one by one to simulte a real-time scene.
Installation#
The stable version can be installed from PyPI:
pip install streamad
The development version can be installed from GitHub:
pip install git+https://github.com/Fengrui-Liu/StreamAD
Quick Start#
Start once detection within 5 lines of code. You can find more example with visualization results here.
from streamad.util import StreamGenerator, UnivariateDS
from streamad.model import SpotDetector
ds = UnivariateDS()
stream = StreamGenerator(ds.data)
model = SpotDetector()
for x in stream.iter_item():
score = model.fit_score(x)
Models#
For univariate time series#
If you want to detect multivarite time series with these models, you need to apply them on each feature separately.
Model Example |
API Usage |
Paper |
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Conformalized density- and distance-based anomaly detection in time-series data |
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For multivariate time series#
These models are compatible with univariate time series.
Models Example |
API Usage |
Paper |
---|---|---|
Subspace Outlier Detection in Linear Time with Randomized Hashing |
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