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Updated: May 6, 2026

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混合ARIMA-LSTM用于COVID-19预测:一个比较的AI建模研究.

Al Mahmud1, Syed Husni Noor Syed Hatim Noor1, Kamarul Imran Musa2

  • 1School of Dental Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia.

PeerJ. Computer science
|September 24, 2025
PubMed
概括
此摘要是机器生成的。

相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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与传统的ARIMA和深度学习LSTM模型相比,混合ARIMA-LSTM模型显著提高了流行病预测的准确性. 这种方法为流行病学提供了更可靠的预测分析.

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:

  • 流行病带来了重大的全球挑战,需要准确的预测.
  • 经典的统计模型 (ARIMA) 难以处理非线性流行病数据.
  • 深度学习模型 (LSTM) 是有前途的,但需要大量的资源.

研究的目的:

  • 为了比较ARIMA,LSTM和混合ARIMA-LSTM模型的预测性能.
  • 通过马来西亚的COVID-19数据来评估模型准确性.
  • 确定流行病趋势预测的最有效的建模方法.

主要方法:

  • 使用自回归集成移动平均 (ARIMA) 和长期短期记忆 (LSTM) 模型.
  • 开发并测试了一种混合动力ARIMA-LSTM模型.
  • 使用诸如MSE,MAE,MAPE,RMSE,RRMSE,NRMSE和R2之类的指标进行绩效评估.

主要成果:

  • 在捕捉大流行趋势方面,ARIMA模型表现不佳.
  • 与ARIMA相比,LSTM模型显示出更好的预测准确性.
  • 混合型ARIMA-LSTM模型始终产生了最低的错误率.
关键词:
预测COVID-19的预测情况混合动力ARIMA-LSTM模型模型机器学习在流行病学中的应用.预测流行病的预测.针对传染病的预测分析.时间序列分析分析时间序列分析

相关实验视频

Last Updated: May 6, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

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结论:

  • 混合ARIMA-LSTM模型提供卓越的流行病预测准确度.
  • 整合统计和深度学习方法可以增强流行病学中的预测分析.
  • 建议采用混合模型,以便可靠地预测流行病和分配资源.