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).
Installation:
Usage:
See examples.
from patternly import AnomalyDetection
Examples:
See examples directory for code and notebooks
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-0.0.2.tar.gz
(5.7 kB
view hashes)
Built Distribution
Close
Hashes for patternly-0.0.2-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fbefdbdefcab623d9a00da69ced1a07f0367032fe5ccd512b4631c0fd0ff41ae |
|
MD5 | 874601754405838f181549bd462e3c72 |
|
BLAKE2b-256 | 1896c89ad18ab6296e444874d72cffdd3d49132cd622c7723b30d627b8ca0ddf |