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基于离散波量变换的混合模型和ARIMA模型在预测COVID-19发病率方面的比较性能

Kourosh Holakouie-Naieni¹, Mojtaba Sepandi², Babak Eshrati³

¹Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

|July 23, 2024

概括

这项研究引入了一种混合模型,将离散波形变换 (DWT) 和自行回归集成移动平均 (ARIMA) 结合起来,用于COVID-19病例预测. 与独立的ARIMA模型相比,混合DWT-ARIMA模型在预测COVID-19发病率方面表现出更高的准确性.

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:

准确的公共卫生监测对于疾病爆发期间的早期预警系统至关重要.
预测模型在预测疾病发病率和为公共卫生战略提供信息方面发挥着至关重要的作用.
随着COVID-19的流行,人们需要强大的模型来预测疾病的传播.

研究的目的:

为了比较混合离散波形变换 (DWT) 和自行回归集成移动平均 (ARIMA) 模型与COVID-19发病率的纯ARIMA模型的预测性能.
评估将DWT与ARIMA结合用于传染病病例时间序列预测的有效性.
使用混合模型评估预测的COVID-19病例数量的准确性.

主要方法:

利用了2019年2月26日至2022年4月25日的每日COVID-19确诊病例的时间序列数据.
开发并应用了一种混合模型,将离散波形变换 (DWT) 集成用于数据分解和ARIMA用于时间序列预测.
将混合DWT-ARIMA模型的预测准确度与使用统计软件的传统ARIMA模型进行了比较.

主要成果:

与独立的ARIMA模型相比,混合DWT-ARIMA模型在预测COVID-19发病率方面表现出更高的准确性.
混合模型产生的预测与实际观察到的发病率数据的相关性更强.
该DWT组件有效地提高了ARIMA模型捕捉COVID-19病例数据的基本趋势的能力.

结论:

将离散波形变换 (DWT) 与ARIMA模型相结合,可以显著改善对COVID-19发病率未来趋势的预测.
混合DWT-ARIMA方法为公共卫生监测和疫情早期预警系统提供了更准确和可靠的方法.
这项研究验证了波形分解在提高ARIMA模型在流行病预测中的性能方面的实用性.

关键词:

在阿里马,阿里马就是阿里马.在 COVID-19 疫情中,这就是为什么DWT DWT DWT 混合动力模型混合动力模型预测预测预测

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