Metadata-Version: 2.3
Name: spotforecast2
Version: 0.0.2
Summary: Forecasting with spot
Author: bartzbeielstein
Author-email: bartzbeielstein <32470350+bartzbeielstein@users.noreply.github.com>
Requires-Dist: astral>=3.2
Requires-Dist: feature-engine>=1.9.3
Requires-Dist: flake8>=7.3.0
Requires-Dist: holidays>=0.90
Requires-Dist: ipykernel>=7.1.0
Requires-Dist: jupyter>=1.1.1
Requires-Dist: lightgbm>=4.6.0
Requires-Dist: matplotlib>=3.10.8
Requires-Dist: numba>=0.63.1
Requires-Dist: optuna>=4.7.0
Requires-Dist: pandas>=3.0.0
Requires-Dist: plotly>=6.5.2
Requires-Dist: pyarrow>=23.0.0
Requires-Dist: scikit-learn>=1.8.0
Requires-Dist: spotoptim>=0.0.160
Requires-Dist: tqdm>=4.67.2
Requires-Python: >=3.13
Description-Content-Type: text/markdown

 # About spotforecast2

`spotforecast2` is a Python library for time series forecasting. 

Parts of the code are ported from `skforecast` to reduce external dependencies.
Many thanks to the [skforecast team](https://skforecast.org/0.20.0/more/about-skforecast.html) for their great work!


# License

`spotforecast2` software: [BSD-3-Clause License](https://github.com/sequential-parameter-optimization/spotforecast2?tab=BSD-3-Clause-1-ov-file)


# References

## skforecast: 

* Amat Rodrigo, J., & Escobar Ortiz, J. (2026). skforecast (Version 0.20.0) [Computer software]. https://doi.org/10.5281/zenodo.8382788 

## spotoptim:

* [spotoptim documentation](https://sequential-parameter-optimization.github.io/spotoptim/)