Model: LockNQuay - LNQ-ens1

    Name: ens1
    Abbreviation: LNQ-ens1
    Owner: covid19hub
    Team name: LockNQuay
    Description: County-level ensemble of boosted tree and neural net models. Lots of engineered features.
    Contributors: Russ Wolfinger (SAS) <russ.wolfinger@sas.com>, David Lander (NQ)
    License: MIT
    Notes: primary
    Citation: https://www.kaggle.com/c/covid19-global-forecasting-week-5/overview
    Methods: 1. The final forecasts are an ensemble of three models, two fit with LightGBM (gradient boosted trees) and a third with Keras (neural net). The models are based on a few of the top models in the Kaggle competitions from last spring. a. The first LightGBM model builds many time-series features from the raw training data. b. The second LightGBM model incorporates some external data, including basic forecasts from SAS Visual Studio and Facebook Prophet, plus some more exotic ones like genomic clade proportions in each location. c. The neural net model is the simplest in terms of features, primarily relying on 28 daily lagged values, but is able to train on case and fatality data all at once. 2. The LightGBM models are trained separately on cases and fatalities using quantile loss for each of the seven quantiles [0.025, 0.100, 0.250, 0.500, 0.750, 0.900, 0.975] on daily data from all major countries worldwide (also from JHU) and all US counties. The neural net is trained using pinball loss over all of these same seven quantiles but separately for each of 28 days into the future. Cubic spline interpolation is used for the death targets to produce forecasts for an additional 16 quantiles. 3. Ensemble weights are chosen by hand each week based on performance in the previous week. The most common weighting scheme is equal, with 1/3 on each model. Other weightings are simple fractions like 0.4, 0.4, 0.2. A hillclimibing analysis (regression with constraint that all coefficients must be nonnegative) is run to provide some guidance. https://www.kaggle.com/c/m5-forecasting-uncertainty/discussion/163368, https://www.kaggle.com/aerdem4/covid-19-w5-pipeline
    Home: https://www.kaggle.com/sasrdw/locknquay
    Auxiliary data: Missing

    Forecasts (48)

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    2021-03-14 2021-03-14-LNQ-ens1.csv 2021-03-16 00:41:22 UTC 2021-03-15 12:00:00 UTC
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    2021-03-21 2021-03-21-LNQ-ens1.csv 2021-03-23 00:40:11 UTC 2021-03-22 12:00:00 UTC
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    2021-03-28 2021-03-28-LNQ-ens1.csv 2021-03-30 00:48:23 UTC 2021-03-29 12:00:00 UTC
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    2021-04-04 2021-04-04-LNQ-ens1.csv 2021-04-06 01:07:52 UTC 2021-04-05 12:00:00 UTC
    2021-04-05 (No data)
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    2021-04-11 2021-04-11-LNQ-ens1.csv 2021-04-13 00:36:08 UTC 2021-04-12 12:00:00 UTC
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    2021-04-18 2021-04-18-LNQ-ens1.csv 2021-04-20 00:54:48 UTC 2021-04-19 12:00:00 UTC
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    2021-04-25 2021-04-25-LNQ-ens1.csv 2021-04-27 00:53:36 UTC 2021-04-26 12:00:00 UTC
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    2021-05-02 2021-05-02-LNQ-ens1.csv 2021-05-04 04:09:22 UTC 2021-05-04 12:00:00 UTC
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    2021-05-09 2021-05-09-LNQ-ens1.csv 2021-05-11 01:32:12 UTC 2021-05-10 12:00:00 UTC
    2021-05-10 (No data)
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    2021-05-16 2021-05-16-LNQ-ens1.csv 2021-05-18 01:15:53 UTC 2021-05-17 12:00:00 UTC
    2021-05-17 (No data)
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    2021-05-23 2021-05-23-LNQ-ens1.csv 2021-05-25 00:55:12 UTC 2021-05-24 12:00:00 UTC
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    2021-05-30 2021-05-30-LNQ-ens1.csv 2021-05-31 19:50:15 UTC 2021-05-31 19:50:15 UTC
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