# Distributionally Robust Optimization with Correlated Data from Vector Autoregressive Processes

arXiv: https://arxiv.org/abs/1909.03433 · HTML: https://mlsanity.com/paper/1909.03433/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [Data-Driven Sample Average Approximation with Covariate Information](https://mlsanity.com/paper/2207.13554/) (2022)
- [Robustifying Conditional Portfolio Decisions via Optimal Transport](https://mlsanity.com/paper/2103.16451/) (2021)
- [Distributionally Robust Prescriptive Analytics with Wasserstein Distance](https://mlsanity.com/paper/2106.05724/) (2021)
- [Distributionally Robust Optimization with Markovian Data](https://mlsanity.com/paper/2106.06741/) (2021)
- [Distributionally Robust Geometric Joint Chance-Constrained Optimization: Neurodynamic Approaches](https://mlsanity.com/paper/2603.06597/) (2026)

## Abstract (arXiv)

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.