Metadata-Version: 2.1
Name: patternly
Version: 0.0.6
Summary: A tool for detecting anomalies in time series data
Home-page: https://github.com/zeroknowledgediscovery/mantis
Author: @zedlab_
Author-email: ishanu@uchicago.edu
License: LICENSE
Download-URL: https://github.com/zeroknowledgediscovery/mantis/archive/0.0.6.tar.gz
Keywords: anomaly detection,timeseries,model-free,adaptive
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: zedsuite

===============
patternly
===============

.. image:: http://zed.uchicago.edu/logo/mantislogo1.png
   :height: 50px
   :alt: mantis logo
   :align: center

.. class:: no-web no-pdf

: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

.. _PFSA: https://pubmed.ncbi.nlm.nih.gov/23277601/


**Installation:**

**Usage:**

See `examples`_.

.. _examples: https://github.com/zeroknowledgediscovery/patternly/tree/main/examples

.. code-block::

    from patternly.detection import AnomalyDetection



