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