We propose FlowMixer, a single-layer architecture using semi-group properties to eliminate neural depth search, achieving competitive multivariate forecasting with interpretable Kronecker-Koopman eigenmodes and algebraic horizon manipulation.

We introduce a modular matrix factorization framework called "covariate-moderated empirical Bayes matrix factorization" (cEBMF) that can leverage side information to improve the factorization through the use of adaptive priors.