Skip to main content

Time series Framework

Project description


TSBoost is a framework for time series forecasting.

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


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

TSBoost uses gradient boosting optimisation provided by XGBoost & LightGBM, both have C++ source code and need a compiler.

For Windows users, VC runtime 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


After installing the compiler, install from PyPI Using pip

pip install tsboost

Quick Start

You can get started with a jupyter notebook tutorial : TSBoot quick start


0.1.0 (2019-06-10)

  • First release on PyPI.

Project details

Release history Release notifications

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for tsboost, version 0.1.0
Filename, size & hash File type Python version Upload date
tsboost-0.1.0-py2.py3-none-any.whl (8.9 kB) View hashes Wheel py2.py3
tsboost-0.1.0.tar.gz (13.9 kB) View hashes Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page