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        "author": "Franck Durand",
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            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
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            "Programming Language :: Python :: 3.5",
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        "description": "TSBoost, Time Series Boosting\n=============================\n\n\nContext\n-------\n\nTSBoost is a framework for time series forecasting.\n\nIt mixes classical statistics practices with non linear optimisation techniques of current Machine Learning.\n\nRequirements\n------------\n\n32-bit Python is not supported. Please install 64-bit version.\n\n\nTSBoost 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.\n\n\nFor **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.\n\n\nFor **Linux** users, **glibc** >= 2.14 is required\n\n    sudo apt-get install build-essential      # Ubuntu/Debian\n\n    sudo yum groupinstall 'Development Tools' # CentOS/RHEL\n\nFor **macOS** users, install OpenMP librairy\n\n    brew install libomp\n\nInstallation\n------------\n\nAfter installing the compiler, install from `PyPI <https://pypi.org/project/tsboost>`_ Using ``pip``\n\n\n    pip install tsboost\n\n\nQuick Start\n-----------\n\nYou can get started with a jupyter notebook tutorial : `TSBoot quick start <https://github.com/franck-durand/tsboost/jupyter/tsboost_quick_start.ipynb>`_\n\n\n\n\n\n=======\nHistory\n=======\n\n0.1.0 (2019-06-10)\n------------------\n\n* First release on PyPI.\n\n\n",
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