{
  "annotation_id": "90a1e32f-e98f-4c54-9eb9-9b139844886c",
  "date_created": "2026-03-02T18:01:17.183000Z",
  "date_modified": "2026-03-02T18:01:17.183000Z",
  "file_hash": "a06cc3669e20d3d3081aa77832313c3bdd5dd4cf84be8cec85c231b8c25ac182",
  "private": false,
  "record": {
    "abstract": "We employ the Bayesian framework to define a cointegration measure aimed to\nrepresent long term relationships between time series. For visualization of\nthese relationships we introduce a dissimilarity matrix and a map based on the\nSorting Points Into Neighborhoods (SPIN) technique, which has been previously\nused to analyze large data sets from DNA arrays. We exemplify the technique in\nthree data sets: US interest rates, monthly inflation rates and gross domestic\nproduct growth rates.",
    "arxiv_id": "physics/0701062",
    "authors": [
      "Renato Vicente",
      "Carlos de B. Pereira",
      "Vitor B. P. Leite",
      "Nestor Caticha"
    ],
    "categories": [
      "physics.soc-ph",
      "physics.data-an",
      "q-fin.ST"
    ],
    "doi": "10.1016/j.physa.2007.02.067",
    "journal_ref": "Physica A 380 (2007) 317-324",
    "title": "Long Term Economic Relationships From Cointegration Maps",
    "url": "https://arxiv.org/abs/physics/0701062"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "ae581816-7590-4754-8594-87080da768cf",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}