多时间序列的变长共有模式挖掘算法  

VARIABLE-LENGTH CONSENSUS MOTIF DISCOVERY IN MULTIPLE TIME SERIES

作　　者：张明明 王鹏[2] 汪卫[2] Zhang Mingming;Wang Peng;Wang Wei(School of Software,Fudan University,Shanghai 200438,China;School of Computer Science,Fudan University,Shanghai 200438,China)

机构地区：[1]复旦大学软件学院,上海200438 [2]复旦大学计算机科学技术学院,上海200438

出　　处：《计算机应用与软件》2025年第3期244-252,共9页Computer Applications and Software

基　　金：国家科技部重点研发计划项目(2020YFB1710001)。

摘　　要：时间序列的模式挖掘是数据挖掘领域的一项重要研究任务,旨在挖掘出序列中相似的且有意义的子序列片段。但对于多时间序列中的变长共有模式挖掘,现有的方法并不能高效地解决该问题。为此,提出一种高效的、可扩展的算法VCMD。该算法通过使用一种新型的下界剪枝方法并结合一种基于频繁项剪枝优化的策略加快了多时间序列中变长共有模式的挖掘速度。在多个真实数据集上的实验结果表明,VCMD与现有的方法相比在时间效率和下界性能上都得到了显著的提升。Time series motif discovery is an important research task in the field of data mining,aiming to find the similar and meaningful sub-sequence fragments in the sequences.However,for the mining of variable-length consensus motif in multiple time series,the existing methods cannot efficiently solve this problem.To this end,an efficient and extensible algorithm VCMD is proposed.This algorithm used a novel lower bound pruning method and combined with a strategy based on frequent item pruning optimization to accelerate the variable-length consensus motif discovery.Experimental results on multiple real data sets show that VCMD can significantly improve the time efficiency and the lower bound performance compared with existing methods.

关 键 词：时间序列 模式挖掘 变长共有模式 下界剪枝 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]