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
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Tip revision: f33bdb42c1c3bddacd65527c296f9dd5d7a38ec2 authored by Antoine Carme on 01 June 2020, 11:33:48 UTC
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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