一种新的不确定性时间序列概率相似查找方法

摘要

针对传统的数据管理中的数据表示、存储与索引、查询与挖掘等所有技术,不能直接应用于不确定性时间序列数据的相似性查找的不足.研究了可用于不确定性时间序列数据的降维表示、索引与剪枝、查找等理论与技术,针对不确定性时间序列数据结构的复杂性,首次给出了不确定性时间序列上的概率最近邻的定义;将不确定性时间序列进行了PLA降维,转换到PLA空间,并提出了三个引理,用以加速查找效率;基于该三个引理,提出了概率K最近邻查找算法PKNNS.通过实验,验证了PKNNS算法的有效性和效率.%The traditional data management,data representation,storage and indexing,querying,mining and all other technical can not be directly applied to the similarity search of time series data with uncertainty.Our work study for other theories and technologies,the uncertainty of time-series data for the complexity of the structure,and for the first time we give the formal definition of probability nearest neighbor search over uncertain time series database; the PLA dimensionality reduction over time series of uncertainty.After conversion to the PLA space,we propose three lemmas to accelerate the search efficiency; based on three lemmas the appropriate searching algorithm PKNNS is also given.A serials of experiments are also made to test the effectiveness and efficiency of algorithm PKNNS.