{
  "annotation_id": "64721049-9085-4338-989f-b2e57e506344",
  "date_created": "2026-03-02T18:01:07.939000Z",
  "date_modified": "2026-03-02T18:01:07.939000Z",
  "file_hash": "c221a008f208633ab9a6c4ba80fdc41a45aa9819eec95bcab14623e4b785d54f",
  "private": false,
  "record": {
    "abstract": "We study the scaling behavior of the fluctuations, as extracted through\nwavelet coefficients based on discrete wavelets. The analysis is carried out on\na variety of physical data sets, as well as Gaussian white noise and binomial\nmulti-fractal model time series and the results are compared with continuous\nwavelet based average wavelet coefficient method. It is found that high-pass\ncoefficients of wavelets, belonging to the Daubechies family are quite good in\nestimating the true power in the fluctuations in a non-stationary time series.\nHence, the fluctuation functions based on discrete wavelet coefficients find\nthe Hurst scaling exponents accurately.",
    "arxiv_id": "physics/0604004",
    "authors": [
      "P. Manimaran",
      "Prasanta K. Panigrahi",
      "Jitendra C. Parikh"
    ],
    "categories": [
      "physics.data-an"
    ],
    "title": "On Estimation of Hurst Scaling Exponent through Discrete Wavelets",
    "url": "https://arxiv.org/abs/physics/0604004"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "25c037bd-72c9-4139-924f-5a314eb810fd",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}