{
  "id": "1801.03523",
  "version": "v1",
  "published": "2018-01-09T03:35:20.000Z",
  "updated": "2018-01-09T03:35:20.000Z",
  "title": "Generative Models for Stochastic Processes Using Convolutional Neural Networks",
  "authors": [
    "Fernando Fernandes Neto"
  ],
  "categories": [
    "stat.ML",
    "cs.NE",
    "physics.comp-ph",
    "q-fin.CP"
  ],
  "abstract": "The present paper aims to demonstrate the usage of Convolutional Neural Networks as a generative model for stochastic processes, enabling researchers from a wide range of fields (such as quantitative finance and physics) to develop a general tool for forecasts and simulations without the need to identify/assume a specific system structure or estimate its parameters.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-01-09T03:35:20.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "convolutional neural networks",
      "stochastic processes",
      "generative model",
      "specific system structure",
      "general tool"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}