| 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) |
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| 2023-02-06 | forecast-553d7cb6fe58.json | 2023-02-09 11:17:42 UTC | 2023-02-09 11:17:42 UTC | |
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