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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:

https://zeroknowledgediscovery.github.io/patternly

Installation:

pip install patternly --user -U

Usage:

See examples.

from patternly.detection import AnomalyDetection, StreamingDetection

Project links

Key dates

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  • Released:
Latest release

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for drewvlaz from gravatar.com drewvlaz Avatar for ishanuc from gravatar.com ishanuc

Credits

Author: @zedlab_

License

MIT License (LICENSE)

Requires

Python >=3.6

Tags

anomaly detection timeseries model-free adaptive

Classifiers

Report project as malware

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