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.
Multi-Variate Time Series Forecasting on Variable Subsets
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
baseline 1
citation-polarity summary
fields
cs.LG 3years
2026 3representative citing papers
ACT disentangles temporal scales in stock sequences and purifies structural relations in graphs to achieve state-of-the-art cross-sectional stock ranking on CSI300 and CSI500 with up to 74.25% improvement.
A scalable framework harmonizes spatial and temporal representations via low-rank spatial compression and extended temporal horizons to reduce prediction uncertainty in large-scale spatiotemporal tasks.
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.
-
ACT: Anti-Crosstalk Learning for Cross-Sectional Stock Ranking via Temporal Disentanglement and Structural Purification
ACT disentangles temporal scales in stock sequences and purifies structural relations in graphs to achieve state-of-the-art cross-sectional stock ranking on CSI300 and CSI500 with up to 74.25% improvement.
-
Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance
A scalable framework harmonizes spatial and temporal representations via low-rank spatial compression and extended temporal horizons to reduce prediction uncertainty in large-scale spatiotemporal tasks.