A tool for detecting anomalies in time series data
Project description
- Info:
Paper draft link will be posted here
- Author:
Drew Vlasnik, Ishanu Chattopadhyay
- Laboratory:
The Laboratory for Zero Knowledge Discovery, The University of Chicago https://zed.uchicago.edu
- Description:
Discovery of emergent anomalies in data streams without explicit prior models of correct or aberrant behavior, based on the modeling of ergodic, quasi-stationary finite valued processes as probabilistic finite state automata (PFSA).
- Documentation:
Installation:
pip install patternly --user -UUsage:
See examples.
from patternly.detection import AnomalyDetection, StreamingDetectionRelease history Release notifications | RSS feed
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