{
  "id": "1909.13316",
  "version": "v1",
  "published": "2019-09-29T16:44:12.000Z",
  "updated": "2019-09-29T16:44:12.000Z",
  "title": "Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters",
  "authors": [
    "Vitor Cerqueira",
    "Luis Torgo",
    "Carlos Soares"
  ],
  "comment": "9 pages",
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "Time series forecasting is one of the most active research topics. Machine learning methods have been increasingly adopted to solve these predictive tasks. However, in a recent work, these were shown to systematically present a lower predictive performance relative to simple statistical methods. In this work, we counter these results. We show that these are only valid under an extremely low sample size. Using a learning curve method, our results suggest that machine learning methods improve their relative predictive performance as the sample size grows. The code to reproduce the experiments is available at https://github.com/vcerqueira/MLforForecasting.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-09-29T16:44:12.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "time series forecasting",
      "size matters",
      "machine learning methods",
      "lower predictive performance",
      "research topics"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 9,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}