The ubiquity of movement data has led to new research interest in aspects of temporal scale. Few of the approaches analyzing movement data have been developed specifically for scale-oriented temporal analysis. To overcome this limitation, this article proposes a series-based approach to perform scale-oriented temporal analysis of movement data. Key to the proposed approach is the construction of four types of series (one type of identical-scale series and three types of cross-scale series) based on the continuous triangular model (CTM). Two distinct research goals are derived from this: to investigate the changes in motion attributes of moving individuals across different temporal scales, and to detect the time intervals during which active events might have occurred. The results based on real football movement data show that finer changes in motion attributes can be found and more accurate time intervals can be detected through the proposed approach.

中文翻译：

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运动数据的面向尺度的时间分析：基于序列的方法

运动数据的普遍存在引发了人们对时间尺度方面的新研究兴趣。很少有分析运动数据的方法是专门为面向尺度的时间分析而开发的。为了克服这一限制，本文提出了一种基于序列的方法来对运动数据执行面向尺度的时间分析。该方法的关键是基于连续三角模型（CTM）构建四种类型的序列（一种同尺度序列和三种跨尺度序列）。由此衍生出两个不同的研究目标：研究在不同时间尺度上移动的个体的运动属性的变化，并检测活动事件可能发生的时间间隔。基于真实足球运动数据的结果表明，通过所提出的方法可以发现运动属性的更精细变化并可以检测到更准确的时间间隔。