{
  "annotation_id": "e31979b0-fe21-496c-879d-8427fe0250fd",
  "date_created": "2026-03-02T18:01:03.870000Z",
  "date_modified": "2026-03-02T18:01:03.870000Z",
  "file_hash": "f9176afad7f22ce583c1e612579c3854cbfb5c71d4794f6bb87c2fac75efbf5e",
  "private": false,
  "record": {
    "abstract": "The present study investigates linear and volatile (nonlinear) correlations\nof first-order autoregressive process with uncorrelated AR (1) and long-range\ncorrelated CAR (1) Gaussian innovations as a function of the process parameter\n($\\theta$). In the light of recent findings \\cite{jano}, we discuss the choice\nof CAR (1) in modeling daily temperature records. We demonstrate that while CAR\n(1) is able to capture linear correlations it is unable to capture nonlinear\n(volatile) correlations in daily temperature records.",
    "arxiv_id": "physics/0512241",
    "authors": [
      "Radhakrishnan Nagarajan",
      "R. B. Govindan"
    ],
    "categories": [
      "physics.ao-ph",
      "cond-mat.stat-mech",
      "physics.geo-ph"
    ],
    "doi": "10.1016/j.physa.2005.12.042",
    "title": "Appropriateness of correlated first order auto-regressive processes for   modeling daily temperature records",
    "url": "https://arxiv.org/abs/physics/0512241"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "cda5248a-4fd3-4094-b7e7-21eb29649a9c",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}