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  "conda_build_version": "3.24.0",
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  "description": "Implements a procedure for forecasting time series data based on an\nadditive model where non-linear trends are fit with yearly, weekly, and\ndaily seasonality, plus holiday effects. It works best with time series\nthat have strong seasonal effects and several seasons of historical data.\nProphet is robust to missing data and shifts in the trend, and typically\nhandles outliers well.\n",
  "dev_url": "https://github.com/facebook/prophet",
  "doc_url": "https://facebook.github.io/prophet/docs/quick_start.html",
  "env_vars": {
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    "copy_test_source_files": true,
    "final": true,
    "flow_run_id": "b09bb945-7826-4393-ad3a-15a50329c730",
    "recipe-maintainers": [
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      "maresb"
    ],
    "remote_url": "git@github.com:AnacondaRecipes/prophet-feedstock.git",
    "sha": "0dfd57ddbdecf1a1878882f1995a0b93933fba26",
    "skip-lints": [
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  "home": "https://facebook.github.io/prophet/",
  "identifiers": [],
  "keywords": [],
  "license": "MIT",
  "license_family": "MIT",
  "license_file": "LICENSE",
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  "summary": "Automatic Forecasting Procedure",
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}