基于模型的时间依赖观测的集群,具有共同的结构变化
Riccardo Corradin1, Luca Danese1, Wasiur R KhudaBukhsh2
1Department of Economics, Management and Statistics, University of Milano-Bicocca, Milano, 20136 Italy.
概括
本研究引入了一种新的对时间序列数据的聚类方法. 它基于同时发生的结构变化的数据进行分组,适用于流行病学建模,比如COVID-19在欧盟的传播.
科学领域:
- 统计 统计 统计 统计
- 计算生物学 计算生物学
- 流行病学 流行病学
背景情况:
- 聚类时间序列数据具有挑战性.
- 现有的方法往往无法捕捉同步的行为变化.
- 识别同时发生的结构变化对于比较分析至关重要.
研究的目的:
- 为时间序列开发一种基于模型的新型聚类方法.
- 根据结构变化的时机对观察结果进行分组.
- 将这种方法应用于流行病学数据,特别是欧盟COVID-19的传播.
主要方法:
- 在时间序列中使用结构变化的潜在表示.
- 采用随机顺序来识别和同步这些变化.
- 开发一种可适应各种时间依赖模型的一般建模策略.
主要成果:
- 展示了一种用于聚类时间序列数据的新方法.
- 成功确定了基于同步结构变化的COVID-19传播动态相似的国家.
- 基于模型的方法提供了灵活性,可以与现有的时间依赖模型集成.
结论:
- 拟议的方法有效地通过结构变化的同步性对时间序列进行集群.
- 这种方法为流行病学分析和政策制定提供了宝贵的见解.
- 一般框架允许广泛应用在分析时间依赖的数据.
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