Metadata-Version: 2.0
Name: tsboost
Version: 0.1.0
Summary: Time series Framework
Home-page: https://github.com/franck-durand/tsboost
Author: Franck Durand
Author-email: franck.durand@gadz.org
License: MIT license
Keywords: tsboost
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/x-rst
Requires-Dist: lightgbm (>=2.2.1)
Requires-Dist: numpy
Requires-Dist: pandas (>=0.22.0)
Requires-Dist: xgboost (>=0.80)

TSBoost, Time Series Boosting
=============================


Context
-------

TSBoost is a framework for time series forecasting.

It mixes classical statistics practices with non linear optimisation techniques of current Machine Learning.

Requirements
------------

32-bit Python is not supported. Please install 64-bit version.


TSBoost uses gradient boosting optimisation provided by `XGBoost <https://github.com/dmlc/xgboost>`_ & `LightGBM <https://github.com/microsoft/LightGBM>`_, both have C++ source code and need a compiler.


For **Windows** users, `VC runtime <https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads>`_ is needed if **Visual Studio** (2015 or newer) is not installed.


For **Linux** users, **glibc** >= 2.14 is required

    sudo apt-get install build-essential      # Ubuntu/Debian

    sudo yum groupinstall 'Development Tools' # CentOS/RHEL

For **macOS** users, install OpenMP librairy

    brew install libomp

Installation
------------

After installing the compiler, install from `PyPI <https://pypi.org/project/tsboost>`_ Using ``pip``


    pip install tsboost


Quick Start
-----------

You can get started with a jupyter notebook tutorial : `TSBoot quick start <https://github.com/franck-durand/tsboost/jupyter/tsboost_quick_start.ipynb>`_





=======
History
=======

0.1.0 (2019-06-10)
------------------

* First release on PyPI.


