cloudpickle
coreforecast>=0.0.11
fsspec
numba
optuna
packaging
pandas
scikit-learn
utilsforecast>=0.2.3
window-ops

[all]
xgboost
pandas<2.2
mlflow>=2.10.0
gitpython
fugue
black>=24
numpy<2
holidays<0.21
shap
ray<2.8
lightgbm
fsspec[s3]
polars[numpy]
pyarrow
ruff
datasetsforecast
setuptools<70
pre-commit
mypy
pyspark>=3.3
matplotlib
xgboost<2
xgboost_ray
fsspec[adl]
nbdev<2.3.26
fugue[ray]
lightgbm_ray
fsspec[gcs]
statsmodels
dask[complete]

[aws]
fsspec[s3]

[azure]
fsspec[adl]

[dask]
fugue
dask[complete]
lightgbm
xgboost

[dev]
black>=24
datasetsforecast
gitpython
holidays<0.21
lightgbm
matplotlib
mlflow>=2.10.0
mypy
nbdev<2.3.26
pre-commit
polars[numpy]
pyarrow
ruff
shap
statsmodels
xgboost

[gcp]
fsspec[gcs]

[lag_transforms]

[polars]
polars[numpy]

[ray]
fugue[ray]
lightgbm_ray
numpy<2
pandas<2.2
ray<2.8
setuptools<70
xgboost<2
xgboost_ray

[spark]
fugue
pyspark>=3.3
lightgbm
xgboost
