A tool for detecting anomalies in time series data
Project description
- Info:
Paper draft link will be posted here
- Author:
ZeD@UChicago <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/patternly/detection.html
Installation:
Usage:
See examples.
from patternly.detection import AnomalyDetection
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-0.0.7.tar.gz
(6.4 kB
view hashes)
Built Distribution
patternly-0.0.7-py3-none-any.whl
(10.6 kB
view hashes)
Close
Hashes for patternly-0.0.7-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 73944e60468bf380113cfdacac77e30da68f41a1d8a9a52ab8d3a8a4424a2cd7 |
|
MD5 | ea67c38a2c81dedab8425da4771a977b |
|
BLAKE2b-256 | e2de020a32be8d73713cbd2369adfc6393aa981eb3bd97cd0c2ccb28f79c40a3 |