Factual package intelligence from PyPI

sforecast 0.6.4 package intelligence

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 mode

pip install sforecast

Current version
0.6.4
Python requirement
<3.13,>=3.10
License
MIT
Distribution type
Pure Python wheel
Release files
2 (1 wheels, 1 source)
Download size
62.2 KiB
Release history
12 releases with files
Median release cadence
6 days

sforecast dependencies

PyPI declares 10 unique dependency rules for this release. Environment markers are shown when supplied by the project.

  1. pandas >=1.4.0
  2. beautifulplots >=0.2.6
  3. tensorflow <=2.11,>2.8.0
  4. statsmodels >=0.13.2
  5. pmdarima >=1.8.0
  6. scikit-learn >=1.4.2
  7. xgboost >=2.0.3
  8. regex <2025.0.0,>=2024.5.15
  9. scipy <2.0.0,>=1.13.1
  10. numpy <2.0.0,>=1.21.2

Compatibility and release files

sforecast publishes 1 wheel and 1 source archive for version 0.6.4. Wheel platform tags: any.

Declared Python classifiers: 3.10.

Metadata completeness

PyPI does not currently declare: project URL. PyDeps marks these fields as unknown instead of guessing values.

Release activity and sources

PyPI lists 12 releases with files. The first dated release is ; 4 releases fall within the 365 days preceding the latest dated release. The current release files were uploaded on . The preceding dated release was .

The project does not publish external project URLs in its PyPI metadata.