计算机工程2009，Vol.35Issue(22)：32-34,37,4.
时间序列周期模式挖掘的周期检测方法
Periodicity Detection Method of Periodic Pattern Mining in Time Series
摘要
Abstract
Periodicity is an important feature for time series that can be used for describing time series exactly and predicting its development trends. In existing mining algorithms for periodic patterns, the periodicity length is user-specified in andvanc, and the presence of noise is not taken into account. Based on ERP(Edit distance with Real Penalty) measurement and time warping algorithm, this paper proposes a novel algorithm for periodicity length detection, which can realize warp on the time axis including extending and translation. It is less affected by noise interference. Experimental results show that the performance of this algorithm is better than existing periodicity detection algorithms.关键词
时间序列/数据挖掘/周期检测/动态时间弯曲Key words
time series/data mining/periodicity detection/Dynamic Time Warping(DTW)分类
信息技术与安全科学引用本文复制引用
王阅,高学东,武森,陈敏..时间序列周期模式挖掘的周期检测方法[J].计算机工程,2009,35(22):32-34,37,4.基金项目
国家自然科学基金资助项目(11260011) （11260011）
教育部新世纪优秀人才支持计划基金资助项目(NCET-05-0097) （NCET-05-0097）
