{
  "id": "1807.09011",
  "version": "v1",
  "published": "2018-07-24T10:15:49.000Z",
  "updated": "2018-07-24T10:15:49.000Z",
  "title": "Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series",
  "authors": [
    "Axel Brando",
    "Jose A. Rodríguez-Serrano",
    "Mauricio Ciprian",
    "Roberto Maestre",
    "Jordi Vitrià"
  ],
  "comment": "17 pages, 5 figures, Applied Data Science Track of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2018)",
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "Deep Learning is a consolidated, state-of-the-art Machine Learning tool to fit a function when provided with large data sets of examples. However, in regression tasks, the straightforward application of Deep Learning models provides a point estimate of the target. In addition, the model does not take into account the uncertainty of a prediction. This represents a great limitation for tasks where communicating an erroneous prediction carries a risk. In this paper we tackle a real-world problem of forecasting impending financial expenses and incomings of customers, while displaying predictable monetary amounts on a mobile app. In this context, we investigate if we would obtain an advantage by applying Deep Learning models with a Heteroscedastic model of the variance of a network's output. Experimentally, we achieve a higher accuracy than non-trivial baselines. More importantly, we introduce a mechanism to discard low-confidence predictions, which means that they will not be visible to users. This should help enhance the user experience of our product.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-07-24T10:15:49.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep networks",
      "noisy series",
      "forecasting short",
      "uncertainty modelling",
      "deep learning models"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 17,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}