论文标题
约翰·霍普金斯大学的COVID-19时序列数据的预测分析
Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University
论文作者
论文摘要
我们使用约翰·霍普金斯大学（Johns Hopkins University）在线公开提供的数据集对Covid-19的传播（也称为SARS-COV-2）的传播提供了预测分析。我们的主要目标是在接下来的14天内对不同国家的受感染人数进行预测。预测分析是使用对数量表转换的时间序列数据进行的。我们使用两种众所周知的方法进行预测：多项式回归和神经网络。由于每个国家 /地区的培训数据数量有限，因此我们使用一个称为Extreme Learning Machine（ELM）的单层神经网络来避免过度拟合。由于时间序列的非平稳性，滑动窗口方法用于提供更准确的预测。
We provide a predictive analysis of the spread of COVID-19, also known as SARS-CoV-2, using the dataset made publicly available online by the Johns Hopkins University. Our main objective is to provide predictions of the number of infected people for different countries in the next 14 days. The predictive analysis is done using time-series data transformed on a logarithmic scale. We use two well-known methods for prediction: polynomial regression and neural network. As the number of training data for each country is limited, we use a single-layer neural network called the extreme learning machine (ELM) to avoid over-fitting. Due to the non-stationary nature of the time-series, a sliding window approach is used to provide a more accurate prediction.
