SensorFault-Bench is a new CPS-grounded benchmark showing that clean-MSE rankings of forecasting models often disagree with their robustness under standardized sensor-fault scenarios across four real datasets.
Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
baseline 1
citation-polarity summary
roles
baseline 1polarities
baseline 1representative citing papers
The paper proposes the IARC-TS protocol that combines drift monitoring, uncertainty quantification, and stress tests to generate reproducible robustness evidence for industrial time series models mapped to EU AI Act obligations.
citing papers explorer
-
Benchmarking Sensor-Fault Robustness in Forecasting
SensorFault-Bench is a new CPS-grounded benchmark showing that clean-MSE rankings of forecasting models often disagree with their robustness under standardized sensor-fault scenarios across four real datasets.
-
Industrial AI Robustness Card for Time Series Models
The paper proposes the IARC-TS protocol that combines drift monitoring, uncertainty quantification, and stress tests to generate reproducible robustness evidence for industrial time series models mapped to EU AI Act obligations.