摘要
0 引言时间序列(Time Series)是指按时间顺序排列的一组数据.对时序数据进行分析,从中获取生成这些数据的系统的相关信息从而完成对系统的模型构造和对系统的未来的行为做出预测,具有重要的价值和意义.
Temporal data mining is one of the important branches of data mining. Current researches on time series in temporal data mining mostly are similarity research. In this paper we propose a way to find fuzzy states evolution patterns and then to extract rules from time series. Experiments show that the patterns and rules obtained are meaningful to predict the tendency of time series.
出处
《计算机科学》
CSCD
北大核心
2002年第3期11-13,共3页
Computer Science
基金
国家教委博士点基金(98069923)
关键词
数据挖掘
数据库
知识发现
时间序列
模糊状态演化模式
Temporal data mining, Similarity search, Fuzzy states evolution patterns, States evolution rules , Time- delay embedding
