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 -U
Usage:
See examples.
from patternly.detection import AnomalyDetection, StreamingDetection
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
patternly-0.0.30.tar.gz
(9.6 kB
view hashes)
Built Distribution
Close
Hashes for patternly-0.0.30-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 072e753af37096906fbc87a386116b255d8834296375f449587c83461863658b |
|
MD5 | 62be2c3b9a8cf1eaed1374d6c6a19adb |
|
BLAKE2b-256 | dfcb88b1b47922a7ceb87c4e4886ff6737787fee6f33737f0d277056ec3c1b44 |