| Name: | CSSE Ensemble |
|---|---|
| Abbreviation: | JHU_CSSE-CSSE_Ensemble |
| Owner: | lshandross |
| Team name: | The Center for Systems Science and Engineering at Johns Hopkins University |
| Description: | Ensemble of time series models including ARIMA, LSTM and GRU |
| Contributors: | Lauren Gardner (Johns Hopkins University) <l.gardner@jhu.edu>, Hongru Du (Johns Hopkins University) <hdu9@jh.edu>, Hao Frank Yang (Johns Hopkins University) <haofrankyang@jhu.edu>, Shaochong Xu (Johns Hopkins University) <sxu75@jh.edu>, Xianglong Wang (Johns Hopkins University) <xwang344@jh.edu>, Yang Zhao (Peking University) <zy_@pku.edu.cn>, Pu Wang (New York University) <pw2425@nyu.edu> |
| License: | Creative Commons Attribution 4.0 |
| Notes: | designated model // data_inputs: Weekly flu hospitalizations, Weekly weather for each states, Google search volume for flu-related symptoms, Change healthcare claims data (accessed via covidcast) |
| Citation: | |
| Methods: | This model predicts state-level influenza hospitalizations through an ensemble of time series forecasting methods at three hierarchical levels: individual state-level forecasts using ARIMA, region-level predictions with Long Short-Term Memory (LSTM) models, and a single Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model that encompasses all states. These three-level models are combined to yield the final ensemble prediction. |
| Home: | https://systems.jhu.edu/ |
| Auxiliary data: | (No URL) |
| Timezero | Data Source | Upload Time | Issued at | Version |
|---|---|---|---|---|
| 2024-04-27 | forecast-73812cbd1f0.json | 2024-04-25 11:22:34 UTC | 2024-04-25 11:22:34 UTC | |
| 2024-04-20 | forecast-734e77a395ea.json | 2024-04-18 11:22:25 UTC | 2024-04-18 11:22:25 UTC | |
| 2024-04-13 | forecast-73e31a6697de.json | 2024-04-12 11:22:44 UTC | 2024-04-12 11:22:44 UTC | |
| 2024-04-06 | forecast-73a712645059.json | 2024-04-04 11:22:24 UTC | 2024-04-04 11:22:24 UTC | |
| 2024-03-30 | forecast-758218a66a60.json | 2024-03-28 11:26:12 UTC | 2024-03-28 11:26:12 UTC | |
| 2024-03-23 | forecast-75557dd43727.json | 2024-03-21 11:26:12 UTC | 2024-03-21 11:26:12 UTC | |
| 2024-03-16 | forecast-75a457b0cf82.json | 2024-03-14 11:25:04 UTC | 2024-03-14 11:25:04 UTC | |
| 2024-03-09 | forecast-75e25bfad4a7.json | 2024-03-07 11:25:23 UTC | 2024-03-07 11:25:23 UTC | |
| 2024-03-02 | forecast-75204594b28f.json | 2024-03-01 15:33:15 UTC | 2024-03-01 15:33:15 UTC | |
| 2024-06-01 | (No data) | |||
| 2024-05-25 | (No data) | |||
| 2024-05-18 | (No data) | |||
| 2024-05-11 | (No data) | |||
| 2024-05-04 | forecast-73ae6b512b8b.json | 2024-05-02 11:22:49 UTC | 2024-05-02 11:22:49 UTC | |
| 2024-02-24 | forecast-74e749fb91a5.json | 2024-02-22 11:24:59 UTC | 2024-02-22 11:24:59 UTC | |
| 2024-02-17 | forecast-75473cb7b9ea.json | 2024-02-15 11:24:44 UTC | 2024-02-15 11:24:44 UTC | |
| 2024-02-10 | forecast-74e813caf14c.json | 2024-02-08 11:25:13 UTC | 2024-02-08 11:25:13 UTC | |
| 2024-02-03 | forecast-710059e27954.json | 2024-02-02 15:48:05 UTC | 2024-02-02 15:48:05 UTC | |
| 2024-01-27 | forecast-7100bc93dd0.json | 2024-02-02 15:48:03 UTC | 2024-02-02 15:48:03 UTC | |
| 2024-01-20 | forecast-7100244c4c7e.json | 2024-02-02 15:48:02 UTC | 2024-02-02 15:48:02 UTC | |
| 2024-01-13 | forecast-71001a87346a.json | 2024-02-02 15:48:01 UTC | 2024-02-02 15:48:01 UTC | |
| 2024-01-06 | (No data) | |||
| 2023-12-30 | (No data) | |||
| 2023-12-23 | (No data) | |||
| 2023-12-16 | (No data) | |||
| 2023-12-09 | (No data) | |||
| 2023-12-02 | (No data) | |||
| 2023-11-25 | (No data) | |||
| 2023-11-18 | (No data) | |||
| 2023-11-11 | (No data) | |||
| 2023-11-04 | (No data) | |||
| 2023-10-28 | (No data) | |||
| 2023-10-21 | (No data) | |||
| 2023-10-14 | (No data) | |||
| 2023-10-07 | (No data) |