一种自动驱动的ESN-DSS方法,用于有效的COVID-19时间序列预测和建模
Weiye Wang1,2, Qing Li1,2, Junsong Wang3
1School of Automation, Beijing Information Science and Technology University, Beijing, China.
Epidemiology and infection
|November 22, 2024
概括
一个新的深度学习模型,Echo State Network-Dove Swarm Search (ESN-DSS),准确地预测了COVID-19时间序列的演变. 这种方法为公共卫生干预和其他非线性时间序列预测挑战提供了可靠的预测.
科学领域:
- 人工智能的人工智能
- 流行病学 流行病学
- 计算科学 计算科学
背景情况:
- 随着COVID-19的流行,全球健康和经济面临重大挑战.
- 准确预测流行病轨迹对于有效的公共卫生战略至关重要.
研究的目的:
- 开发一个简单的深度学习模型来预测COVID-19时间序列演变.
- 评估模型的性能与现有的人工智能方法对比.
主要方法:
- 将群搜索 (DSS) 算法与回声状态网络 (ESN) 集成,以优化模型重量.
- 开发一个自动驱动的ESN-DSS模型,用于时间序列预测,具有封闭的反循环.
- 自动驱动ESN-DSS的参数优化,以提高预测准确度.
主要成果:
- ESN-DSS模型在多个国家的COVID-19时间演变方面表现出色的预测性能.
- 该模型的表现优于一些既有的人工智能预测方法,包括RNN,LSTM,GRU和VAE.
- 调整网络参数显著提高了预测准确度.
结论:
- 自动驱动的ESN-DSS模型为COVID-19时间序列预测提供了强大而准确的方法.
- 这些发现可以为政府和医疗机构制定预防措施提供信息.
- ESN-DSS方法适用于COVID-19以外的更广泛的非线性时间序列预测问题.
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