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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 24·5 min read
GroupSegment-SHAP: Shapley Value Explanations with Group-Segment Players for Multivariate Time Series
TL;DR
GroupSegment-SHAP (GS-SHAP) enhances the interpretability of multivariate time-series models by using group-segment players to capture cross-variable interactions over time. It improves explanation accuracy by 1.7x and reduces runtime by 40% compared to existing SHAP methods.
✦ Why It Matters
Implement GS-SHAP to improve the interpretability of your multivariate time-series models today.
Key Takeaways
- Use GS-SHAP for better interpretability in multivariate time-series analysis.
- Leverage group-segment players to capture variable interactions over time.
- Expect a 1.7x improvement in explanation accuracy with GS-SHAP.
- Reduce computational time by 40% when using GS-SHAP compared to other SHAP methods.
How It Works
GS-SHAP constructs explanatory units by grouping variables that interact over specific time segments, allowing for a more holistic understanding of their joint contributions. By applying Shapley attribution, it quantifies the impact of these group-segment players, ensuring that the explanations reflect the true dynamics of the multivariate time series.