论文标题
COVID-19使用多变量长期记忆的增长预测
COVID-19 growth prediction using multivariate long short term memory
论文作者
论文摘要
冠状病毒病（COVID-19）扩散预测是追踪大流行的生长的重要任务。现有的预测仅基于定性分析和数学建模。即使数据的可用性是丰度，可用的大数据在Covid-19增长预测中仍然受到限制。为了使用深度学习在预测中使用大数据，我们使用长期的短期记忆（LSTM）方法来学习COVID-19的相关性随着时间的流逝。启发LSTM层的结构，直到达到最佳验证分数为止。首先，我们培训了包含来自全球各地的确认病例的培训数据。与复发性神经网络（RNN）方法相比，我们实现了有利的性能，并具有可比的低验证误差。评估是根据图形可视化和根平方误差（RMSE）进行的。我们发现，随着时间的流逝，实现相同数量的确认案例并不容易。但是，LSTM在实际情况和预测之间提供了类似的模式。将来，我们提出的预测可用于预测即将到来的大流行。该代码在此处提供：https：//github.com/cbasemaster/lstmcorona
Coronavirus disease (COVID-19) spread forecasting is an important task to track the growth of the pandemic. Existing predictions are merely based on qualitative analyses and mathematical modeling. The use of available big data with machine learning is still limited in COVID-19 growth prediction even though the availability of data is abundance. To make use of big data in the prediction using deep learning, we use long short-term memory (LSTM) method to learn the correlation of COVID-19 growth over time. The structure of an LSTM layer is searched heuristically until the best validation score is achieved. First, we trained training data containing confirmed cases from around the globe. We achieved favorable performance compared with that of the recurrent neural network (RNN) method with a comparable low validation error. The evaluation is conducted based on graph visualization and root mean squared error (RMSE). We found that it is not easy to achieve the same quantity of confirmed cases over time. However, LSTM provide a similar pattern between the actual cases and prediction. In the future, our proposed prediction can be used for anticipating forthcoming pandemics. The code is provided here: https://github.com/cbasemaster/lstmcorona
