LeapTS reformulates forecasting as adaptive multi-horizon scheduling via hierarchical control and NCDEs, delivering at least 7.4% better performance and 2.6-5.3x faster inference than Transformer baselines while adapting to non-stationary dynamics.
Econometrica: journal of the Econometric Society , pages=
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A weighted l1-regularized estimator for high-dimensional multivariate VAR that incorporates spatial graph constraints to recover sparse spatio-temporal transition structures.
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LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling
LeapTS reformulates forecasting as adaptive multi-horizon scheduling via hierarchical control and NCDEs, delivering at least 7.4% better performance and 2.6-5.3x faster inference than Transformer baselines while adapting to non-stationary dynamics.
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High-Dimensional Multivariate VAR Estimation with Spatio-Temporal Structure
A weighted l1-regularized estimator for high-dimensional multivariate VAR that incorporates spatial graph constraints to recover sparse spatio-temporal transition structures.