一个新的自动回归多变量修改自动编码器用于多变量时间序列预测:一个案例研究,适用于COVID-19流行病
Emerson Vilar de Oliveira1, Dunfrey Pires Aragão1, Luiz Marcos Garcia Gonçalves1
1Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Av. Salgado Filho, 3000, Campus Universitário, Lagoa Nova, Natal 59078-970, RN, Brazil.
这项研究引入了一种新的堆叠自动编码模型,用于改进时间序列预测,在预测COVID-19趋势和环境因素方面表现优于现有的方法.
科学领域:
- 流行病学 流行病学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 由于数据限制,COVID-19大流行突出了准确预测的挑战.
- 现有的流行病学和机器学习模型显示出有效性,但对多变量流行病数据的精度限制.
研究的目的:
- 提出和评估一种新的堆叠自动编码器方法,用于增强时间序列预测.
- 为了解决流行病情景的多变量预测的局限性.
主要方法:
- 开发了一种新的时间序列预测方法,使用堆叠的自动编码器结构,用于训练和体重调整的变化.
- 使用COVID-19病例数据,环境因素 (温度,湿度,AQI) 和全球病例百分比进行了比较实验.
主要成果:
- 在整个数据中,根平均平方误差 (RMSE) 降低了80.7%,在50个经过试验训练的模型中,测试数据降低了10.3%.
- 一个特定的模型变化 (模型类型#3) 排名第4的整体,超过了像NBEATS,Prophet和Glounts这样的既定模型.
结论:
- 拟议的堆叠自动编码器模型展示了显著的预测能力和多功能性.
- 这种方法对各种时间序列任务有希望,特别是在复杂的场景中,如流行病预测.
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