Distribution-based anomaly detection for time series.
Project description
Distribution-based anomaly detection for time series.
>>> from fossa import LastStepPredictor
...
1 Installation
pip install fossa
2 Features
Adheres to the scikit-learn classifier API.
Pickle-able classifier objects.
Pure python.
Supports Python 3.5+.
Fully tested.
3 Use
TBA
4 Contributing
Current package maintainer (and one of the authors) is Shay Palachy (shay.palachy@gmail.com); You are more than welcome to approach him for help. Contributions are very welcomed.
4.1 Installing for development
Clone:
git clone git@github.com:shaypal5/fossa.git
Install in development mode, including test dependencies:
cd fossa
pip install -e '.[test]'
4.2 Running the tests
To run the tests use:
cd fossa
pytest
4.3 Adding documentation
The project is documented using the numpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings. When documenting code you add to this project, follow these conventions.
Additionally, if you update this README.rst file, use python setup.py checkdocs to validate it compiles.
5 Credits
Created by Shay Palachy (shay.palachy@gmail.com) and Omri Mendels.
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 Distributions
Built Distribution
Hashes for fossa-0.0.1-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ea3d51ac54baf3cc909380bf635c1fb89e730420d2feef55d29e65c671ccdb38 |
|
MD5 | e20edfd654cb5dda8cecfed2335cf938 |
|
BLAKE2b-256 | bd29d7c5090a2d479e4942dfc89ae989c5f9be0df898c326b4c440080bda2975 |