Model: ReichLab - Kernel Conditional Density Estimation, post-hoc backfill adjustment API

    Name: Kernel Conditional Density Estimation, post-hoc backfill adjustment
    Abbreviation: KCDE_PHB
    Owner: kgle
    Project: CDC Real-time Forecast
    Team name: ReichLab
    Description: Team name: ReichLab
    Team members: Evan L. Ray, Graham C. Gibson, Nicholas G. Reich
    Data source(s): ilinet
    Methods: Used leave-one-season-out cross-validation in the training phase to estimate 1) parameters of kernel conditional density estimation (KCDE) models for incidence h steps ahead, for h = 1, ..., 35; and 2) parameters of a copula C^H modeling dependence structure across the next H weeks, for H = 2, ..., 35. Together, these yield a joint predictive distribution for incidence in all remaining weeks of the season. Appropriate integrals of this joint distribution are calculated via Monte Carlo integration to obtain predictions for the seasonal quantities. For making prospective predictions for each season, only data before the start of that season were used in fitting model parameters. After predictions are made based on currently available ilinet data, a post-hoc adjustment for backfill is made by sampling possible revisions to data. These adjustments affect forecasts for seasonal targets only. All code used in estimation and prediction is available at https://github.com/reichlab/cdcfluutils and https://github.com/reichlab/cdcfluforecasts
    Home: https://github.com/FluSightNetwork/cdc-flusight-ensemble/tree/master/model-forecasts/component-models/ReichLab_kcde_backfill_post_hoc
    Auxiliary data: (No auxiliary data)

    Forecasts (3)

    Timezero Data Source Upload Date
    2017-10-23 (No data)
    2017-10-30 (No data)
    2017-11-06 (No data)
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    2019-10-14 EW42-2019-ReichLab_kcde_backfill_post_hoc.csv 2019-10-31 08:18:22
    2019-10-21 EW43-2019-ReichLab_kcde_backfill_post_hoc.csv 2019-11-06 11:47:35
    2019-10-28 EW44-2019-ReichLab_kcde_backfill_post_hoc.csv 2019-11-15 08:50:02
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