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Fengrui Liu edited this page May 26, 2022
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Anomaly detection for data streams/time series. Detectors process the univariate or multivariate data one by one to simulte a real-time scene.
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
Start once detection within 5 lines of code. You can find more example with visualization results here.
from streamad.util import StreamGenerator, UnivariateDS, plot
from streamad.model import SpotDetector
ds = UnivariateDS()
stream = StreamGenerator(ds.data)
model = SpotDetector()
for x in stream.iter_item():
score = model.fit_score(x)
If you want to detect multivarite time series with these models, you need to apply them on each feature separately.
These models are compatible with univariate time series.