An improved symbolic aggregate approximation distance measure based on its statistical features

Chaw Thet Zan, H. Yamana
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引用次数: 35

Abstract

The challenges in efficient data representation and similarity measures on massive amounts of time series have enormous impact on many applications. This paper addresses an improvement on Symbolic Aggregate approXimation (SAX), is one of the efficient representations for time series mining. Because SAX represents its symbols by the average (mean) value of a segment with the assumption of Gaussian distribution, it is insufficient to serve the entire deterministic information and causes sometimes incorrect results in time series classification. In this work, SAX representation and distance measure is improved with the addition of another moment of the prior distribution, standard deviation; SAX_SD is proposed. We provide comprehensive analysis for the proposed SAX_SD and confirm both the highest classification accuracy and the highest dimensionality reduction ratio on University of California, Riverside (UCR) datasets in comparison to state of the art methods such as SAX, Extended SAX (ESAX) and SAX Trend Distance (SAX_TD).
一种改进的基于统计特征的符号聚集近似距离测度
在大量时间序列上有效的数据表示和相似性度量方面的挑战对许多应用产生了巨大的影响。本文对时间序列挖掘的一种有效表示——符号聚合近似(SAX)进行了改进。由于SAX是用假设为高斯分布的段的平均值(均值)来表示其符号,因此它不足以服务于整个确定性信息,并且在时间序列分类中有时会导致错误的结果。在这项工作中,通过增加先验分布的另一个时刻,即标准差,改进了SAX的表示和距离度量;提出SAX_SD。我们对提议的SAX_SD进行了全面的分析,并与最先进的方法如SAX、扩展SAX (ESAX)和SAX趋势距离(SAX_TD)相比,在加州大学河滨分校(UCR)的数据集上确认了最高的分类精度和最高的降维率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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