Model: Carnegie Mellon Delphi Group - CMU-TimeSeries

    Name: TimeSeries
    Abbreviation: CMU-TimeSeries
    Owner: covid19hub
    Team name: Carnegie Mellon Delphi Group
    Description: (State) A basic AR-type time series model fit using case counts and deaths as features. (County) Quantile regr. on county pop., case counts, self-reported symptom data, and doctor's visits.
    Contributors: Alden Green (Lead modeler - state, Carnegie Mellon University), <ajgreen@andrew.cmu.edu>, Addison Hu (Co-lead modeler - county, Carnegie Mellon University), Maria Jahja (Co-lead modeler - county, Carnegie Mellon University), Valerie Ventura (Carnegie Mellon University), Larry Wasserman (Carnegie Mellon University), Rob Tibshirani (Stanford University), Vishnu Shankar (Stanford University), Jacob Bien (University of Southern California), Logan Brooks (Carnegie Mellon University), Balasubramanian Narasimhan (Stanford University), Samyak Rajanala (Stanford University), Aaron Rumack (Carnegie Mellon University), Noah Simon (University of Washington), James Sharpnack (UC Davis), Daniel McDonald (University of British Columbia), Ryan Tibshirani (Senior author, Carnegie Mellon University), and the Delphi COVID-19 Response Team
    License: Creative Commons Attribution 4.0
    Notes: primary
    Citation: Missing
    Methods: State level - A basic AR-type time series model fit using lagged values of case counts and deaths as features. No assumptions are made regarding reopening or governmental interventions. The model is jointly fit across all 50 US states, after some time-alignment is performed as a preprocessing step to facilitate comparison between states. Heavier weight is placed on more recent training data, to account for nonstationarity in the underlying process. A lasso penalty is added to induce variable selection and prevent overfitting. Quantiles are computed using a residual (Gaussian) bootstrap, separately for each location. County level - A quantile regression model fit jointly across selected counties using (1) case counts, (2) self-reported COVID symptom rates, (3) doctor's visits rates related to COVID symptoms, and (4) county population. Features (1-3) are taken at various lags. Furthermore, the slopes of features (1-3) are computed over the preceding month and included in the model to provide a sense of the virus's momentum. Signals (2-3) are available as indicators through Delphi COVIDcast (http://covidcast.cmu.edu). Signal (2) is administered with the support of Facebook. No assumptions are made regarding reopening or governmental interventions.
    Home: https://delphi.cmu.edu
    Auxiliary data: Missing

    Forecasts (53)

    Timezero Data Source Upload Time Issued at Version
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    2020-08-02 2020-08-02-CMU-TimeSeries.csv 2020-08-04 01:21:31 UTC 2020-08-04 12:00:00 UTC
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    2020-07-20 2020-07-20-CMU-TimeSeries.csv 2020-07-21 02:59:16 UTC 2020-07-21 12:00:00 UTC
    2020-07-27 2020-07-27-CMU-TimeSeries.csv 2020-07-28 00:59:12 UTC 2020-07-28 12:00:00 UTC
    2020-08-03 (No data)
    2020-08-10 2020-08-10-CMU-TimeSeries.csv 2020-08-11 00:53:39 UTC 2020-08-11 12:00:00 UTC
    2020-08-17 2020-08-17-CMU-TimeSeries.csv 2020-08-18 18:43:50 UTC 2020-08-18 12:00:00 UTC
    2020-08-24 2020-08-24-CMU-TimeSeries.csv 2020-08-25 00:54:29 UTC 2020-08-25 12:00:00 UTC
    2020-08-31 2020-08-31-CMU-TimeSeries.csv 2020-09-01 01:01:55 UTC 2020-09-01 12:00:00 UTC
    2020-09-07 2020-09-07-CMU-TimeSeries.csv 2020-09-08 01:12:07 UTC 2020-09-08 12:00:00 UTC
    2020-09-14 2020-09-14-CMU-TimeSeries.csv 2020-09-15 00:53:37 UTC 2020-09-15 12:00:00 UTC
    2020-09-21 2020-09-21-CMU-TimeSeries.csv 2020-09-22 00:55:25 UTC 2020-09-22 12:00:00 UTC
    2020-09-28 2020-09-28-CMU-TimeSeries.csv 2020-09-29 06:25:45 UTC 2020-09-29 12:00:00 UTC
    2020-10-05 2020-10-05-CMU-TimeSeries.csv 2020-10-06 12:37:38 UTC 2020-10-06 12:00:00 UTC
    2020-10-12 2020-10-12-CMU-TimeSeries.csv 2020-10-13 02:00:12 UTC 2020-10-13 12:00:00 UTC
    2020-10-19 2020-10-19-CMU-TimeSeries.csv 2020-10-20 19:15:25 UTC 2020-10-20 12:00:00 UTC
    2020-10-26 2020-10-26-CMU-TimeSeries.csv 2020-10-27 01:44:01 UTC 2020-10-27 12:00:00 UTC
    2020-11-02 2020-11-02-CMU-TimeSeries.csv 2020-11-03 06:53:29 UTC 2020-11-03 12:00:00 UTC
    2020-11-09 2020-11-09-CMU-TimeSeries.csv 2020-11-11 21:53:47 UTC 2020-11-11 12:00:00 UTC
    2020-11-16 2020-11-16-CMU-TimeSeries.csv 2020-11-17 00:35:23 UTC 2020-11-17 12:00:00 UTC
    2020-11-23 2020-11-23-CMU-TimeSeries.csv 2020-11-24 06:21:28 UTC 2020-11-24 12:00:00 UTC
    2020-11-30 2020-11-30-CMU-TimeSeries.csv 2020-12-01 06:34:32 UTC 2020-12-01 12:00:00 UTC
    2020-12-07 2020-12-07-CMU-TimeSeries.csv 2020-12-08 00:35:06 UTC 2020-12-07 12:00:00 UTC
    2020-12-14 2020-12-14-CMU-TimeSeries.csv 2020-12-15 00:45:51 UTC 2020-12-14 12:00:00 UTC
    2020-12-21 2020-12-21-CMU-TimeSeries.csv 2020-12-22 16:09:48 UTC 2020-12-22 12:00:00 UTC
    2020-12-28 2020-12-28-CMU-TimeSeries.csv 2020-12-29 06:35:05 UTC 2020-12-29 12:00:00 UTC
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    2021-07-05 2021-07-05-CMU-TimeSeries.csv 2021-07-06 01:26:42 UTC 2021-07-06 01:26:42 UTC
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    2021-07-12 2021-07-12-CMU-TimeSeries.csv 2021-07-13 01:05:50 UTC 2021-07-13 01:05:50 UTC
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    2021-01-04 2021-01-04-CMU-TimeSeries.csv 2021-01-05 06:35:53 UTC 2021-01-05 12:00:00 UTC
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    2021-01-10 2021-01-10-CMU-TimeSeries.csv 2021-01-12 06:38:25 UTC 2021-01-12 12:00:00 UTC
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    2021-01-18 2021-01-18-CMU-TimeSeries.csv 2021-01-19 06:57:40 UTC 2021-01-19 12:00:00 UTC
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    2021-01-25 2021-01-25-CMU-TimeSeries.csv 2021-01-26 12:37:16 UTC 2021-01-26 12:00:00 UTC
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    2021-02-01 2021-02-01-CMU-TimeSeries.csv 2021-02-02 00:36:09 UTC 2021-02-01 12:00:00 UTC
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    2021-03-01 2021-03-01-CMU-TimeSeries.csv 2021-03-02 00:40:21 UTC 2021-03-01 12:00:00 UTC
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    2021-03-08 2021-03-08-CMU-TimeSeries.csv 2021-03-09 01:36:22 UTC 2021-03-08 12:00:00 UTC
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    2021-04-12 2021-04-12-CMU-TimeSeries.csv 2021-04-13 04:06:14 UTC 2021-04-13 12:00:00 UTC
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    2021-04-26 2021-04-26-CMU-TimeSeries.csv 2021-04-27 00:49:18 UTC 2021-04-26 12:00:00 UTC
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