Skip to main content

Time series Framework

Project description

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 & 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

Installation

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

History

0.1.0 (2019-06-10)

  • First release on PyPI.

Project details


Download files

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

Source Distribution

tsboost-0.1.0.tar.gz (13.9 kB view details)

Uploaded Source

Built Distribution

tsboost-0.1.0-py2.py3-none-any.whl (8.9 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file tsboost-0.1.0.tar.gz.

File metadata

  • Download URL: tsboost-0.1.0.tar.gz
  • Upload date:
  • Size: 13.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.9.1 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.5.2

File hashes

Hashes for tsboost-0.1.0.tar.gz
Algorithm Hash digest
SHA256 87e1214b117d2d980209fcf53e470153f8bf0a48d683d89f2a900e485969a409
MD5 25506942923a2b6659554f70dab742bc
BLAKE2b-256 c647cdd3a352e1746673cb3a897414fd6ad1211674b19950efdb717ec1da445b

See more details on using hashes here.

File details

Details for the file tsboost-0.1.0-py2.py3-none-any.whl.

File metadata

  • Download URL: tsboost-0.1.0-py2.py3-none-any.whl
  • Upload date:
  • Size: 8.9 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.9.1 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.5.2

File hashes

Hashes for tsboost-0.1.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 2064bf72abf23a2a750b3c48fc948fb2847e3f47b50d5443892a98e4d2429ebb
MD5 87720783d0a2c9963412c9abbe240164
BLAKE2b-256 e69d540598f950993ae01fdb1570e44be21d27850a3ffeda1c27d3ead407e2e9

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page