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DOI:
Journal of Software:2004.15(11):1671-1679

在线分割时间序列数据
李爱国,覃征
(西安科技大学,计算机科学技术系,陕西,西安,710054;西安交通大学,计算机科学技术系,陕西,西安,710049)
On-Line Segmentation of Time-Series Data
LI Ai-Guo,QIN Zheng
()
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Received:July 02, 2003    Revised:February 05, 2004
> 中文摘要: 时间序列分割是时间序列数据挖掘研究的重要任务之一.它主要有两个应用:检测生成时间序列的系统何时发生变化;创建时间序列的高级数据表示,从而对时间序列进行索引、聚类和分类.在实时时间序列数据挖掘应用中,需要在线时间序列分割算法,以便实时发现和预测时态模式.在对时间序列分割问题进行形式化描述的基础上,提出了一种评估时间序列的分割结果以及分割算法性能的评价指标,并提出了一种在线分割时间序列数据的递推算法(on-line segmentation,简称OLS).OLS的一个显著特点是不依赖有关时间序列的先验知识.实验结果说明,OLS算法能够有效地在线检测出数据挖掘应用中感兴趣的关键变化点,而且"过拟合"程度低.
中文关键词: 数据挖掘  知识获取  时间序列  分割
Abstract:Segmentation of time series is one of the important tasks in time series data mining. Segmentation has two major uses: It may be performed either to detect when the system that creates the time series has changed or to create a high level representation of the time series for indexing, clustering, and classification. Approaches to on-line segmentation of time series are necessary when identifying and predicting temporal patterns in real-time time series databases are needed, and this is the focus of this paper. A formal description of segmenting time series problem and a criterion for the evolution of segmentation algorithms are presented. An on-line iterative algorithm of segmenting time series, called OLS (on-line segmentation), is then proposed. OLS is independent of a priori knowledge about the segmented time series. Experimental results demonstrate that OLS can on-line detect the critical change points of time series with less 憃ver fit?than that of competitive algorithms.
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基金项目:Supported by the Key Science-Technology Project of the 'Tenth Five-Year-Plan' of Shaan'xi Province of China under Grant No.2000K08-G12 (陕西省科学技术发展计划"十五"攻关项目) Supported by the Key Science-Technology Project of the 'Tenth Five-Year-Plan' of Shaan'xi Province of China under Grant No.2000K08-G12 (陕西省科学技术发展计划"十五"攻关项目)
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李爱国,覃征.在线分割时间序列数据.软件学报,2004,15(11):1671-1679

LI Ai-Guo,QIN Zheng.On-Line Segmentation of Time-Series Data.Journal of Software,2004,15(11):1671-1679