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时间序列相似模式的分层匹配
引用本文:张旻,张燕平,程家兴.时间序列相似模式的分层匹配[J].计算机辅助设计与图形学学报,2005,17(7):1480-1485.
作者姓名:张旻  张燕平  程家兴
作者单位:1. 解放军电子工程学院科研部,合肥,230037;徽大学计算智能与信号处理教育部重点实验室,合肥,230039
2. 徽大学计算智能与信号处理教育部重点实验室,合肥,230039
基金项目:国家自然科学基金(60175018),国家自然科学基金国际合作项目(60111120662)
摘    要:首先将时间序列经EMD分解成细节部分和趋势部分,对低频趋势部分的序列数据进行线性分段近似表示,完成对序列数据的压缩,并将其变换成一种0-1串的形式,以适应趋势序列的快速匹配;然后通过对趋势序列模式聚类,达到对序列的粗匹配;最后对粗匹配的序列进行距离计算,从而获取细匹配的模式.实验结果表明该算法是有效的.

关 键 词:时间序列  趋势序列  模式匹配  经验模式分解

Hierarchical Algorithm to Match Similar Time Series Pattern
Zhang Min,Zhang Yanping,Cheng Jiaxing.Hierarchical Algorithm to Match Similar Time Series Pattern[J].Journal of Computer-Aided Design & Computer Graphics,2005,17(7):1480-1485.
Authors:Zhang Min  Zhang Yanping  Cheng Jiaxing
Affiliation:Zhang Min1,2) Zhang Yanping2) Cheng Jiaxing2) 1)
Abstract:A time series is first decomposed into a trend part and some detail parts via empirical mode decomposition. Then the trend part is represented in the form of piecewise linear segments to reduce its dimensionality and these segments are transformed further into a 0-1 string to fit the fast matching algorithm. After clustering the transformed trend series, rough similar time series will be obtained. Finally by calculating the distance of the clustered series, accurate similar series patterns are reached. Experiments show that this hierarchical approach is effective.
Keywords:time series  trend series  pattern matching  empirical mode decomposition
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