SARS-CoV-2全球大流行促使各国政府,机构,和研究人员调查其影响,根据一般指标制定策略,以做出最精确的预测。使用了基于流行病学模型的方法,但由于数据不足或缺失,结果表明预测具有不确定性。除了缺乏数据,机器学习模型,包括随机森林,支持向量回归,LSTM,自动编码器,和传统的时间序列模型,如先知和ARIMA被用于任务,取得显著成果,效果有限。其中一些方法在处理多变量输入时具有精度约束,这对需要短期和长期预测的流行病等问题很重要。鉴于在这种情况下供应不足,我们提出了一种基于堆叠自动编码器结构的时间序列预测的新方法,使用相同模型的三个变体进行训练步骤和权重调整,以评估其预测性能。我们与以前发表的关于COVID-19病例的数据进行了比较实验,死亡,温度,湿度,湿度圣保罗市的空气质量指数(AQI),巴西。此外,我们使用了截至5月4日全球十大受影响国家的COVID-19病例百分比,2020年。结果显示,在50个试验训练模型的分布上,整个和测试数据的RMSE下降了80.7%和10.3%,分别,与第一个实验比较。此外,型号#3取得了第四好的整体排名表现,克服NBEATS,先知,并在第二个实验中对时间序列模型进行了比较。该模型显示出在不同输入数据集长度上有前途的预测能力和多功能性,使其成为时序任务的突出预测模型。
The SARS-CoV-2 global pandemic prompted governments, institutions, and researchers to investigate its impact, developing strategies based on general indicators to make the most precise predictions possible. Approaches based on epidemiological models were used but the outcomes demonstrated
forecasting with uncertainty due to insufficient or missing data. Besides the lack of data, machine-learning models including random forest, support vector regression, LSTM, Auto-encoders, and traditional time-series models such as Prophet and ARIMA were employed in the task, achieving remarkable results with limited effectiveness. Some of these methodologies have precision constraints in dealing with multi-variable inputs, which are important for problems like pandemics that require short and long-term
forecasting. Given the under-supply in this scenario, we propose a novel approach for time-series prediction based on stacking auto-encoder structures using three variations of the same model for the training step and weight adjustment to evaluate its
forecasting performance. We conducted comparison experiments with previously published data on COVID-19 cases, deaths, temperature, humidity, and air quality index (AQI) in São Paulo City, Brazil. Additionally, we used the percentage of COVID-19 cases from the top ten affected countries worldwide until May 4th, 2020. The results show 80.7% and 10.3% decrease in RMSE to entire and test data over the distribution of 50 trial-trained models, respectively, compared to the first experiment comparison. Also, model type#3 achieved 4th better overall ranking performance, overcoming the NBEATS, Prophet, and Glounts time-series models in the second experiment comparison. This model shows promising forecast capacity and versatility across different input dataset lengths, making it a prominent
forecasting model for time-series tasks.