{
  "annotation_id": "ef667c77-0d3e-4d20-b18f-3122dcc85ff6",
  "date_created": "2026-03-02T18:00:39.481000Z",
  "date_modified": "2026-03-02T18:00:39.481000Z",
  "file_hash": "30e4aaa00d45795ddda691459b976ef301508c2b9456bb9d249823da9b59379a",
  "private": false,
  "record": {
    "abstract": "Fractal behavior and long-range dependence have been observed in tele-traffic\nmeasurement and characterization. In this paper we show results of application\nof the fractal analysis to internet traffic via various methods. Our result\ndemonstrate that the internet traffic exhibits self-similarity. Time-scale\nanalysis show to be an effective way to characterize the local irregularity.\nBased on the result of this study, these two Internet time series exhibit\nfractal characteristic with long-range dependence.",
    "arxiv_id": "physics/0206012",
    "authors": [
      "K. B. Chong",
      "K. Y. Choo"
    ],
    "categories": [
      "physics.comp-ph",
      "physics.data-an"
    ],
    "title": "Fractal Analysis On Internet Traffic Time Series",
    "url": "https://arxiv.org/abs/physics/0206012"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "28c174d0-1394-4de5-bc42-b9f9f20cb7a2",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}