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Author:
Jan Beitner
Summary:
Forecasting timeseries with PyTorch - dataloaders, normalizers, metrics and models
Latest version:
1.6.1
Required dependencies:
lightning
|
numpy
|
pandas
|
scikit-base
|
scikit-learn
|
scipy
|
torch
Optional dependencies:
black
|
coverage
|
cpflows
|
docutils
|
invoke
|
ipykernel
|
ipywidgets
|
lightning
|
matplotlib
|
mypy
|
nbconvert
|
nbsphinx
|
networkx
|
numpy
|
optuna
|
optuna-integration
|
pandas
|
pandoc
|
pre-commit
|
pyarrow
|
pydata-sphinx-theme
|
pydocstyle
|
pylint
|
pytest
|
pytest-cov
|
pytest-dotenv
|
pytest-github-actions-annotate-failures
|
pytest-sugar
|
pytest-xdist
|
pytorch_optimizer
|
recommonmark
|
ruff
|
scikit-base
|
scikit-learn
|
scipy
|
sphinx
|
statsmodels
|
tensorboard
|
torch
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