Model: The University of Michigan - UMich-RidgeTfReg

    Name: RidgeTensorFlowRegressor
    Abbreviation: UMich-RidgeTfReg
    Owner: covid19hub
    Team name: The University of Michigan
    Description: Nation-level model of confirmed cases and deaths based on ridge regression. No assumptions made about social distancing.
    Contributors: Sabrina Corsetti (University of Michigan Department of Physics) <sabcorse@umich.edu>, Thomas Schwarz (University of Michigan Department of Physics) <schwarzt@umich.edu>, Robert Myers (University of Michigan Department of Physics) <romyers@umich.edu>,
    License: Creative Commons Attribution 4.0
    Notes: primary
    Citation: Missing
    Methods: This model uses ridge regression (penalized Ordinary Least Squares regression) to make predictions about future confirmed case and mortality trends. The model makes forecasts based solely on previous data, so no external assumptions are necessary. The ridge regressors built for this model use Finite Impulse Response (FIR) filtration to predict each day's confirmed cases and deaths as a function of the counts on previous days. Specifically, each day's counts are predicted as a linear combination of the counts on a user-specified number of previous days. The goal of the regressors is the determination of an optimal coefficient vector for these predictive linear combinations. The model currently makes predictions based on the previous 15 days. Once a spectrum of forecasts is produced, a TensorFlow module is used to create quantile predictions based off of the accumulated projections. Finally, a gradient boosting regressor is used to provide a least squares fit to the projections and is used to provide point predictions.
    Home: https://gitlab.com/sabcorse/covid-19-collaboration
    Auxiliary data: Missing

    Forecasts (17)

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