基于曲率距离的时间序列相似性搜索方法

针对几种时间序列相似性度量方法存在的序列元素值依赖性,对序列信息挖掘不充分等问题,该文提出一种新的时间序列分段、近似表示和相似性度量方法。在对序列信息和规律充分挖掘的基础上,对时间序列进行分段并建立了各分段的精确拟合模型,用分段的拟合曲线在各时刻处曲率组成的曲率序列对原时间序列进行近似表示,给出了时间序列的曲率距离定义。最后,提出了基于曲率距离的时间序列相似性搜索算法。该方法充分挖掘了序列信息,对时间序列的主要形态特征进行了有效保留和识别,经实验验证了该方法的有效性、稳定性和准确性。

Similarity Search Method in Time Series Based on Curvature Distance

In view of shortcomings of some methods for similarity measurement,like value dependent of series’ elements and insufficient mining of information in series,a new method for time series compartmentation,approximation representation and similar measurement is proposed in this paper.Based on sufficient mining of information and orderliness in series,the time series are divided into many sections and the curve fitting model of each section is established.Then,the time series are represented approximately with a sequence of the curvatures of each time in the sections,while the curvature distance is proposed.Finally,the similarity searching algorithms in time series based on curvature distance is proposed.It mines the information of the series sufficiently,retains and recognizes the major shape of the series effectively,experimental results prove the effectiveness,stability and accuracy of the method proposed in this paper.