一种新的在线多任务学习,用于COVID-19多输出时空预测
Zipeng Wu1, Chu Kiong Loo1, Unaizah Obaidellah1
1Faculty of Computer Science & Information Technology, University of Malaya,Kuala Lumpur, 50603, Malaysia.
Heliyon
|August 28, 2023
概括
本研究提出了一种新的机器学习模型,通过解决时间自相关性,空间依赖性和概念漂移来准确预测COVID-19趋势. 这种新的算法提高了流行病预测的预测准确度.
科学领域:
- 流行病学 流行病学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 预测COVID-19趋势对于公共卫生决策至关重要,但因时间自相关性,空间依赖性和概念漂移而复杂.
- 现有的机器学习方法难以同时应对流行病预测中的这些挑战.
研究的目的:
- 开发一种新的在线多任务回归算法,能够处理时间自相关性,空间依赖性和COVID-19趋势预测中的概念漂移.
- 提高流行病预测模型的准确性和适应性.
主要方法:
- 开发了一种在线多任务回归算法,结合了空间依赖的链结构,用于概念漂移适应的ADWIN漂移探测器,以及用于时间自相关的滞后时间序列特征.
- 进行了比较实验,使用加利福尼亚州20个地区的每日确诊的COVID-19病例.
主要成果:
- 与现有方法相比,拟议的模型在COVID-19数据中的概念漂移方面表现出更好的适应性.
- 有效地捕捉了不同地区的空间依赖性,从而提高了预测准确性.
- 超越了先进的批量机器学习模型,如N-Beats,DeepAR,TCN和LSTM.
结论:
- 这种新的算法成功地解决了COVID-19趋势预测中的关键挑战,提供了更好的准确性和适应性.
- 这种方法为流行病预测和公共卫生战略制定提供了更强大的工具.
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