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.
Title resolution pending
1 Pith paper cite this work, alongside 835 external citations. Polarity classification is still indexing.
1
Pith paper citing it
835
external citations · OpenAlex
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
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.