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Deep COVID-19 Forecasting for Multiple States with Data Augmentation

arXiv 2302.01155 ↗First posted 2 Feb 2023cs.LGcited by 0

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Abstract (arXiv)

In this work, we propose a deep learning approach to forecasting state-level COVID-19 trends of weekly cumulative death in the United States (US) and incident cases in Germany. This approach includes a transformer model, an ensemble method, and a data augmentation technique for time series. We arrange the inputs of the transformer in such a way that predictions for different states can attend to the trends of the others. To overcome the issue of scarcity of training data for this COVID-19 pandemic, we have developed a novel data augmentation technique to generate useful data for training. More importantly, the generated data can also be used for model validation. As such, it has a two-fold advantage: 1) more actual observations can be used for training, and 2) the model can be validated on data which has distribution closer to the expected situation. Our model has achieved some of the best state-level results on the COVID-19 Forecast Hub for the US and for Germany.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 44 references that are in the map. First posted on arXiv 2 Feb 2023.

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18 of 44 references.

References · 18

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6 more
  1. 2017A Multi-Horizon Quantile Recurrent Forecastercited by 211
  2. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 169
  3. 2018Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problemscited by 32
  4. 2020Interpretable Sequence Learning for COVID-19 Forecastingcited by 22
  5. 2020Inter-Series Attention Model for COVID-19 Forecastingcited by 18
  6. 2021DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecastingcited by 11

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