funBIalign:基于平均平方残留分数的功能动机发现的层次算法
Jacopo Di Iorio1, Marzia A Cremona2,3, Francesca Chiaromonte1,4,5
1Department of Statistics, Penn State University, Joab L. Thomas Building, University Park, 16802 PA USA.
Statistics and computing
|December 13, 2024
概括
这项研究引入了funBIalign,这是一种用于在数据中寻找反复出现的模式 (功能动机) 的新方法. 它有效地在单个或多个数据集中发现这些动机,在功能数据分析中显示出希望.
科学领域:
- 功能数据分析功能数据分析
- 统计模式识别 统计模式识别
- 时间序列分析时间序列分析.
背景情况:
- 动机发现对于理解功能数据中的复杂模式至关重要.
- 现有的方法可能难以识别单个和多个可能错位的曲线中的图案.
- 需要强大的算法来检测功能数据集中的反复出现的形状.
研究的目的:
- 介绍和评估funBIalign,一种用于功能动机发现和评估的新方法.
- 用添加式模型框架来定义功能性图案.
- 为了证明该方法在模拟和现实世界功能数据上的适用性.
主要方法:
- funBIalign采用了一个多步骤程序,灵感来自集群和双重集群.
- 它使用集聚性层次聚类,具有完整的链接.
- 基于平均平方残留分数的功能距离是动机发现过程的核心.
主要成果:
- 通过广泛的模拟来评估funBIalign的性能.
- funBIalign与其他最近的功能性动机发现方法进行了比较.
- 该方法成功地确定了食品价格通胀和温度变化的现实世界案例研究中的功能性动机.
结论:
- funBIalign提供了一种有效的方法,用于在不同的数据集中发现功能性动机.
- 该方法在模拟和实际应用中展示了强大的性能.
- 这项工作为功能数据分析中的模式识别提供了有价值的工具.
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