https://github.com/antoinecarme/pyaf
Revision f33bdb42c1c3bddacd65527c296f9dd5d7a38ec2 authored by Antoine Carme on 01 June 2020, 11:33:48 UTC, committed by Antoine Carme on 01 June 2020, 11:33:48 UTC
Added 'Seasonal_WeekOfMonth' and 'Seasonal_DayOfNthWeekOfMonth',
1 parent 79d7961
Tip revision: f33bdb42c1c3bddacd65527c296f9dd5d7a38ec2 authored by Antoine Carme on 01 June 2020, 11:33:48 UTC
Analyze Business Seasonals (WeekOfMonth and derivatives) #137
Analyze Business Seasonals (WeekOfMonth and derivatives) #137
Tip revision: f33bdb4
setup.py
from setuptools import setup
from setuptools import find_packages
with open("README.md", "r") as fh:
pyaf_long_description = fh.read()
setup(name='pyaf',
version='1.2.4',
description='Python Automatic Forecasting',
long_description=pyaf_long_description,
long_description_content_type="text/markdown",
author='Antoine CARME',
author_email='antoine.carme@laposte.net',
url='https://github.com/antoinecarme/pyaf',
license='BSD 3-clause',
packages=find_packages(include=['pyaf', 'pyaf.*']),
python_requires='>=3',
classifiers=['Development Status :: 5 - Production/Stable',
'Programming Language :: Python :: 3'],
keywords='arx automatic-forecasting autoregressive benchmark cycle decomposition exogenous forecasting heroku hierarchical-forecasting horizon jupyter pandas python scikit-learn seasonal time-series transformation trend web-service',
install_requires=[
'scipy',
'pandas',
'sklearn',
'matplotlib',
'pydot',
'dill',
'sqlalchemy'
])
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