{
  "annotation_id": "0aba4b28-e3c4-4755-9c1d-b28d02595a77",
  "date_created": "2026-03-02T18:00:39.702000Z",
  "date_modified": "2026-03-02T18:00:39.702000Z",
  "file_hash": "6777c6d5e51b2aff8b88e2a302eb09efc8d80094bd4dca86acfb6770757865c0",
  "private": false,
  "record": {
    "abstract": "We develop a method for the multifractal characterization of nonstationary\ntime series, which is based on a generalization of the detrended fluctuation\nanalysis (DFA). We relate our multifractal DFA method to the standard partition\nfunction-based multifractal formalism, and prove that both approaches are\nequivalent for stationary signals with compact support. By analyzing several\nexamples we show that the new method can reliably determine the multifractal\nscaling behavior of time series. By comparing the multifractal DFA results for\noriginal series to those for shuffled series we can distinguish multifractality\ndue to long-range correlations from multifractality due to a broad probability\ndensity function. We also compare our results with the wavelet transform\nmodulus maxima (WTMM) method, and show that the results are equivalent.",
    "arxiv_id": "physics/0202070",
    "authors": [
      "Jan W. Kantelhardt",
      "Stephan A. Zschiegner",
      "Eva Koscielny-Bunde",
      "Armin Bunde",
      "Shlomo Havlin",
      "H. Eugene Stanley"
    ],
    "categories": [
      "physics.data-an",
      "cond-mat.stat-mech"
    ],
    "doi": "10.1016/S0378-4371(02)01383-3",
    "journal_ref": "Physica A 316, 87 (2002)",
    "title": "Multifractal detrended fluctuation analysis of nonstationary time series",
    "url": "https://arxiv.org/abs/physics/0202070"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "db51a480-53bc-40c0-a99a-2cfbdac9affd",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}