VARMA models are now computationally practical for high-dimensional time series because optimization cost no longer scales with series length—enabling better forecasts than VAR models on real data.
This paper solves a long-standing computational problem in time series modeling: estimating VARMA models (which capture dependencies efficiently) at scale. The key innovation is making each optimization step independent of series length by using Fourier-based sufficient statistics and a reparametrization that ensures valid models by construction.