CAST is a successor-local operator for causal forecasting of simplex-valued time series that retrieves empirical successors from causal context, stabilizes them with a persistence anchor, and applies bounded local stochastic transport while preserving the simplex by construction.
Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
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.
CGM-JEPA learns transferable CGM representations via predictive self-supervised pretraining on unlabeled time series and cross-view distributional objectives, outperforming baselines on AUROC for insulin resistance and beta-cell dysfunction across modality shifts and cohorts.
A single-layer architecture called FlowMixer uses constrained matrix operations and a semi-group property to enable depth-agnostic, interpretable spatiotemporal forecasting with direct eigenmode extraction.
citing papers explorer
-
CAST: Causal Anchored Simplex Transport for Distribution-Valued Time Series
CAST is a successor-local operator for causal forecasting of simplex-valued time series that retrieves empirical successors from causal context, stabilizes them with a persistence anchor, and applies bounded local stochastic transport while preserving the simplex by construction.
-
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.
-
CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining
CGM-JEPA learns transferable CGM representations via predictive self-supervised pretraining on unlabeled time series and cross-view distributional objectives, outperforming baselines on AUROC for insulin resistance and beta-cell dysfunction across modality shifts and cohorts.
-
FlowMixer: A Depth-Agnostic Neural Architecture for Interpretable Spatiotemporal Forecasting
A single-layer architecture called FlowMixer uses constrained matrix operations and a semi-group property to enable depth-agnostic, interpretable spatiotemporal forecasting with direct eigenmode extraction.