Model: UMass-Amherst - UMass-gbq

    Name: gbq
    Abbreviation: UMass-gbq
    Owner: lshandross
    Team name: UMass-Amherst
    Description: Gradient boosting for the median, followed by conformal prediction to get quantiles
    Contributors: Serena Wang, Evan Ray <elray@umass.edu>
    License: Creative Commons Attribution 4.0
    Notes: secondary
    Citation:
    Methods: Bagged gradient boosting is used to obtain a predictive median. Features include the day of week, location, population, and summaries of timeseries behavior. Quantiles are obtained via a procedure similar to split conformal prediction, where quantiles of the out-of-bag residuals are added to the point predictions obtained from gradient boosting. Forecasts are created at the daily scale, and then aggregated to weekly. Model training is done jointly for COVID-19 and influenza hospitalizations
    Home: https://github.com/reichlab/covid-hosp-models
    Auxiliary data: (No URL)

    Forecasts (9)

    Timezero Data Source Upload Time Issued at Version
    2022-10-03 (No data)
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    2023-02-06 forecast-553d7cb6fe58.json 2023-02-09 11:17:42 UTC 2023-02-09 11:17:42 UTC
    2023-01-30 forecast-54996cd6e763.json 2023-02-02 20:06:11 UTC 2023-02-02 20:06:11 UTC
    2023-01-23 forecast-551a6edc2b64.json 2023-01-25 23:20:09 UTC 2023-01-25 23:20:09 UTC
    2023-01-16 forecast-550f46a0efdc.json 2023-01-24 14:43:54 UTC 2023-01-24 14:43:54 UTC
    2023-01-09 forecast-5c2c3b297971.json 2023-01-13 19:35:39 UTC 2023-01-13 19:35:39 UTC
    2023-01-02 (No data)
    2022-12-26 forecast8044385b5bc3.json 2023-01-03 21:41:39 UTC 2023-01-03 21:41:39 UTC
    2022-12-19 forecast80443328ec6.json 2023-01-03 21:41:37 UTC 2023-01-03 21:41:37 UTC
    2022-12-12 forecast804462f03ff6.json 2023-01-03 21:41:35 UTC 2023-01-03 21:41:35 UTC
    2022-12-05 forecast80447544758a.json 2023-01-03 21:41:32 UTC 2023-01-03 21:41:32 UTC
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