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

Documentation

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

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)

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.