Distributionally Robust Optimization with Correlated Data from Vector Autoregressive Processes

Xialiang Dou; Mihai Anitescu

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arXiv · 1909.03433

Distributionally Robust Optimization with Correlated Data from Vector Autoregressive Processes

Xialiang Dou·Mihai Anitescu

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Abstract

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.

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BibTeX RIS

Xialiang Dou, Mihai Anitescu. 2019-09-08. Distributionally Robust Optimization with Correlated Data from Vector Autoregressive Processes. https://arxiv.org/abs/1909.03433

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