ShaTS computes Shapley attributions directly on semantic groups of time-series features, improving sensor- and process-level anomaly explanations over post hoc SHAP on the SWaT dataset.
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ShaTS: A Shapley-based Explainability Method for Time Series Artificial Intelligence Models applied to Anomaly Detection in Industrial Internet of Things
ShaTS computes Shapley attributions directly on semantic groups of time-series features, improving sensor- and process-level anomaly explanations over post hoc SHAP on the SWaT dataset.