{
  "id": "2107.01705",
  "version": "v1",
  "published": "2021-07-04T18:39:27.000Z",
  "updated": "2021-07-04T18:39:27.000Z",
  "title": "Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality",
  "authors": [
    "Grzegorz Dudek"
  ],
  "comment": "International Work Conference on Artificial Neural Networks IWANN 2021",
  "categories": [
    "cs.LG",
    "cs.NE"
  ],
  "abstract": "This work contributes to the development of neural forecasting models with novel randomization-based learning methods. These methods improve the fitting abilities of the neural model, in comparison to the standard method, by generating network parameters in accordance with the data and target function features. A pattern-based representation of time series makes the proposed approach useful for forecasting time series with multiple seasonality. In the simulation study, we evaluate the performance of the proposed models and find that they can compete in terms of forecasting accuracy with fully-trained networks. Extremely fast and easy training, simple architecture, ease of implementation, high accuracy as well as dealing with nonstationarity and multiple seasonality in time series make the proposed model very attractive for a wide range of complex time series forecasting problems.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-07-04T18:39:27.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "forecasting time series",
      "multiple seasonality",
      "randomized neural networks",
      "complex time series forecasting problems",
      "target function features"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}