settings.ini

[DEFAULT]
host = github
lib_name = mlforecast
user = Nixtla
description = Scalable machine learning based time series forecasting
keywords = python forecast forecasting machine-learning dask
author = José Morales
author_email = jmoralz92@gmail.com
copyright = Nixtla
branch = main
version = 1.0.0
min_python = 3.9
audience = Developers
language = English
custom_sidebar = True
license = apache2
status = 4
requirements = cloudpickle coreforecast>=0.0.15 fsspec optuna pandas scikit-learn utilsforecast>=0.2.9
dask_requirements = fugue dask[complete] lightgbm xgboost
ray_requirements = fugue[ray] lightgbm_ray xgboost_ray
spark_requirements = fugue pyspark>=3.3 lightgbm xgboost
aws_requirements = fsspec[s3]
gcp_requirements = fsspec[gcs]
azure_requirements = fsspec[adl]
polars_requirements = polars[numpy]
dev_requirements = black>=24 datasetsforecast>=1 gitpython holidays<0.21 lightgbm matplotlib mlflow>=2.10.0 mypy nbdev<2.3.26 numpy>=2 pandas>=2.2.2 pre-commit polars[numpy] pyarrow ruff setuptools statsmodels xgboost
nbs_path = nbs
doc_path = _docs
recursive = True
doc_host = https://Nixtla.github.io
doc_baseurl = /mlforecast/
git_url = https://github.com/Nixtla/mlforecast
lib_path = mlforecast
title = mlforecast
tst_flags = polars ray shap window_ops
black_formatting = True
readme_nb = index.ipynb
allowed_metadata_keys = 
allowed_cell_metadata_keys = 
jupyter_hooks = True
clean_ids = True
clear_all = False
put_version_in_init = True