Variational Latent Basis Modeling
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VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecasting
arXiv:2606.02138v1 Announce Type: new Abstract: Out of distribution (OOD) events in multivariate time series forecasting are rare but often dominate real world risk, making average case forecasting insufficient for reliable deployment. Under standard average risk training on mixed ID/OOD distributions, optimization signals from rare OOD events can be overwhelmed by frequent in distribution (ID) patterns, so strong benchmark accuracy may not translate into reliability under high impact...
Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting
Announce Type: replace Abstract: Time series forecasting remains a challenging problem due to the intricate entanglement of intra-period fluctuations and inter-period trends. While recent advances have attempted to reshape 1D sequences into 2D period-phase representations, they suffer from two principal limitations.
CoFi-UCGen: Coarse-to-Fine Unsupervised Conditional Generation without Label Priors
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Deep learning four decades of human migration
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