{
  "id": "1909.03433",
  "version": "v1",
  "published": "2019-09-08T11:30:04.000Z",
  "updated": "2019-09-08T11:30:04.000Z",
  "title": "Distributionally Robust Optimization with Correlated Data from Vector Autoregressive Processes",
  "authors": [
    "Xialiang Dou",
    "Mihai Anitescu"
  ],
  "categories": [
    "math.OC",
    "cs.LG",
    "stat.CO",
    "stat.ML",
    "stat.OT"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-09-08T11:30:04.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "distributionally robust optimization",
      "vector autoregressive processes",
      "correlated data",
      "finite convex-concave saddle point problem",
      "stochastic optimization problem"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}