Similarity measure for heterogeneous multivariate time-series

F. Duchêne, C. Garbay, V. Rialle
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引用次数: 15

Abstract

Defining the similarity of objects is crucial in any data analysis and decision-making process. For those which effectively deal with moving objects, the main issue becomes the comparison of trajectories, also referred to as time-series. Moreover, complex applications may require an object to be a multidimensional vector of heterogeneous parameters. In that paper, we propose a similarity measure for heterogeneous multivariate time-series using a non-metric distance based on the Longest Common Subsequence (LCSS). The proposed definition allows for imprecise matches, outliers, stretching and global translating of the sequences in time. We demonstrate the relevance of our approach in the context of identifying similar behaviors of a person at home.
异构多元时间序列的相似性度量
定义对象的相似性在任何数据分析和决策过程中都是至关重要的。对于那些有效处理运动物体的方法,主要问题是轨迹的比较,也称为时间序列。此外,复杂的应用程序可能要求对象是异构参数的多维向量。在这篇论文中,我们提出了一种基于最长公共子序列(LCSS)的非度量距离的异构多元时间序列相似性度量方法。所提出的定义允许不精确匹配、异常值、扩展和序列的全局翻译。我们证明了我们的方法在识别一个人在家里的类似行为的背景下的相关性。
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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