  We present a distributionally robust formulation of a stochastic optimization
problem for non-i.i.d vector autoregressive data. We use the Wasserstein
distance to define robustness in the space of distributions and we show, using
duality theory, that the problem is equivalent to a finite convex-concave
saddle point problem. The performance of the method is demonstrated on both
synthetic and real data.
