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一种新的ARIMA-LSTM综合方法用于评估COVID-19病例和未来疫情的准备

Somit Jain¹, Shobhit Agrawal¹, Eshaan Mohapatra¹

¹School of Computer Science and Engineering, Vellore Institute of Technology Vellore India.

Health care science

|December 30, 2024

概括

结合自动回归集成移动平均 (ARIMA) 和长短期记忆 (LSTM) 网络的新混合模型提供了卓越的COVID-19病例预测. 这种先进的方法提高了公共卫生规划和资源分配的准确性.

科学领域:

流行病学流行病学
数据科学数据科学数据科学
公共卫生公共卫生

背景情况:

随着COVID-19的流行,全球公共卫生和经济面临重大挑战.
对COVID-19确诊病例的时间序列分析对于了解疾病传播至关重要.
分析了来自印度,巴西,俄罗斯和美国的数据.

研究的目的:

为COVID-19确诊病例开发一个更准确的预测模型.
为了比较混合ARIMA-LSTM模型与既定时间序列模型的性能.
加强对公共卫生危机的准备和应对策略.

主要方法:

开发了一个混合模型,集成自动回归集成移动平均线 (ARIMA) 和长短期记忆 (LSTM).
混合模型利用ARIMA用于线性趋势和LSTM用于非线性依赖.
性能与基线模型进行了评估,包括ARIMA,Gated Recurrent Unit (GRU),LSTM和Prophet.

主要成果:

混合ARIMA-LSTM模型实现了2.4%的平均绝对百分比误差 (MAPE),超过了所有基准模型.
门式循环单元 (GRU) 在基线模型中表现最好,MAPE为2.9%.
与混合方法相比,LSTM实现了3.6%的MAPE,表明精度较低.

结论:

与单个模型和现有的混合方法相比,ARIMA-LSTM混合模型对COVID-19病例的预测准确度更高.
该模型的有效性在多个国家使用各种准确度指标进行了验证.
改进的预测可以导致更好的公共卫生准备,资源分配和干预策略.

关键词:

一个年龄,一个年龄.在阿里马,阿里马就是阿里马.根据COVID-19的情况,在这里,GRU GRU GRU 这是LSTM的LSTM.预言者预言者预言者预言者拟议的模型拟议的模型时间序列时间序列

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