基于导数序列的时间序列同构关系发现  

Homogeneous pattern discovery of time series based on derivative series

作　　者：邹蕾[1] 高学东[1] 

机构地区：[1]北京科技大学东凌经济管理学院,北京100083

出　　处：《计算机应用》2016年第9期2472-2474,2496,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(71272161)~~

摘　　要：时间序列子序列匹配作为时间序列检索、聚类、分类、异常监测等挖掘任务的基础被广泛研究。但传统的时间序列子序列匹配都是对精确相同或近似相同的模式进行匹配,为此定义了一种全新的具有相似发展趋势的序列模式——时间序列同构关系,经过数学推导给出了时间序列同构关系判定的法则,并基于此提出了同构关系时间序列片段发现的算法。该算法首先对原始时间序列进行预处理,然后分段拟合后对各时间序列分段进行同构关系判定。针对现实背景数据难以满足理论约束的问题,通过定义一个同构关系容忍度参数使实际时间序列数据的同构关系挖掘成为可能。实验结果表明,该算法能有效挖掘出满足同构关系的时间序列片段。As the basis of time series data mining tasks, such as indexing, clustering, classification, and anomaly detection, subsequence matching has been researched widely. Since the traditional time series subsequence matching only aims at matching the exactly same or approximately same patterns, a new sequence pattern with similar tendency, called time series homogeneous pattern, was defined. With mathematical derivation, the time series homogeneous pattern judgment rules were given, and an algorithm on time series homogeneous pattern discovery was proposed based on those rules. Firstly, the raw time series were preprocessed. Secondly, the homogeneous patterns were matched with segmentation and fitting subsequences. Since practical data can not satisfy the theoretical constraints, a parameter of homogeneous pattern tolerance was defined to make it possible for the practical data homogeneous patterns mining. The experimental results show that the proposed algorithm can effectively mine the time series homogeneous patterns.

关 键 词：时间序列 数据挖掘 子序列匹配 分段 模式发现 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]