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Scientifically driven Model-Agnostic Temporal Feature Importance Analysis

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Compatibility Python Version Linux Compatible
Docs Documentation Status
Meta Ruff Checked with mypy License
Build Tools Hatch project
CI/CD pre-commit.ci status GitHub Actions Coverage Status License and Dependency Check
Security OpenSSF Best Practices Security: Bandit

TemporalScope is an open-source Python package designed to bridge the gap between scientific research and practical industry applications for analyzing the temporal dynamics of feature importance in AI & ML time series models. Developed in alignment with Linux Foundation standards and licensed under Apache 2.0, it builds on tools such as Boruta-SHAP and SHAP, using modern window partitioning algorithms to tackle challenges like non-stationarity and concept drift.

TemporalScope is an open-source Python package designed to bridge the gap between scientific research and practical industry applications for analyzing the temporal dynamics of feature importance in AI & ML time series models. Developed in alignment with Linux Foundation standards and licensed under Apache 2.0, it builds on tools such as Boruta-SHAP and SHAP, using modern window partitioning algorithms to tackle challenges like non-stationarity and concept drift.

This package is flexible and extensible, supporting frameworks like Pandas, Polars, Modin, Dask, and PyArrow via native Narwhals compatibility. Additionally, the optional Clara LLM modules (etymology from the word Clarity) are intended to serve as a model-validation tool to support explainability efforts (XAI).

Note

TemporalScope is currently in beta and pre-release phase, so some installation methods may not work as expected on all platforms.