Model: UMass-Amherst - UMass-trends_ensemble

    Name: Ensemble of baseline models with trends
    Abbreviation: UMass-trends_ensemble
    Owner: covid19hub
    Team name: UMass-Amherst
    Description: Equally weighted ensemble of simple time-series baseline models.
    Contributors: Nutcha Wattanachit, Aaron Gerding, Nick Reich <nick@umass.edu>, Evan Ray <elray@umass.edu>
    License: Creative Commons Attribution 4.0
    Notes: secondary
    Citation: Missing
    Methods: Equally weighted ensemble of simple time-series baseline models. Each baseline model calculates first differences of incidence in recent weeks. These differences are sampled and then added to the most recently observed incidence. Variations on this method include (a) including the first differences and the negative of these differences to enforce symmetry, resulting in a flat-line forecast, (b) varying the number of time-units in the past for computing the first differences (typically 14, 21, or 28 days) to focus on capturing recent trends, and (c) using the original time-series or a variance-stabilizing transformation of it, e.g. square-root. Additionally, the resulting predictive distributions are truncated so that any predicted samples computed to be less than zero are truncated to be zero.
    Home: https://github.com/reichlab/covid-hosp-models
    Auxiliary data: Missing

    Forecasts (47)

    Timezero Data Source Upload Time Issued at Version
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    2022-01-24 2022-01-24-UMass-trends_ensemble.csv 2022-01-25 00:37:09 UTC 2022-01-25 00:37:09 UTC
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    2022-04-11 2022-04-11-UMass-trends_ensemble.csv 2022-04-11 18:38:57 UTC 2022-04-11 18:38:57 UTC
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