Metadata-Version: 2.1
Name: sforecast
Version: 0.4.3
Summary: A framework for running forecasting models within a sliding/expanding window out-of-sample forecast fit (train/test) and prediction (forecasts). The package includes support of classical forecasting models, SK Learn supervised learning ML models, and TensorFlow deep learning models.
License: MIT
Author: Alberto Gutierrez
Requires-Python: >=3.8,<3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: beautifulplots (>=0.2.6)
Requires-Dist: pandas (>=1.4.0)
Requires-Dist: pmdarima (>=1.8.0)
Requires-Dist: sklearn (>=0.0)
Requires-Dist: statsmodels (>=0.13.2)
Requires-Dist: tensorflow (>=2.7.0)
Description-Content-Type: text/markdown

# sforecast

A framework for running forecasting models within a sliding (expanding) window out-of-sample fit (train/test) and prediction (forecasts). The package includes support of classical forecasting models, SK Learn ML models, and TensorFlow deep learning models.

## Installation

```bash
$ pip install sforecast
```

## License

`sforecast` was created by Alberto Gutierrez. It is licensed under the terms of the MIT license.

## Credits

`sforecast` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).

