MercerFlow: Flow Matching in a Kernel-Induced Latent Space for Probabilistic Forecasting

Excerpt

arXiv:2610.06039v1 Announce Type: cross Abstract: Recent work has shown that probabilistic flow matching for time series forecasting benefits from a data-matched prior. The resulting prior introduces local correlations, which a sequential architecture usually absorbs: a recurrent neural network (RNN), a structured state-space model (S4), or a Transformer. However, such a backbone costs GPU memory and time per epoch. A cheaper alternative is MLP-based latent-space flow matching: embed the time se