This work presents an approach to support the visual analysis of parameter dependencies of time‐series segmentation. The goal is to help analysts understand which parameters have high influence and which segmentation properties are highly sensitive to parameter changes. Our approach first derives features from the segmentation output and then calculates correlations between the features and the parameters, more precisely, in parameter subranges to capture global and local dependencies. Dedicated overviews visualize the correlations to help users understand parameter impact and recognize distinct regions of influence in the parameter space. A detailed inspection of the segmentations is supported by means of visually emphasizing parameter ranges and segments participating in a dependency. This involves linking and highlighting, and also a special sorting mechanism that adjusts the visualization dynamically as users interactively explore individual dependencies. The approach is applied in the context of segmenting time series for activity recognition. Informal feedback from a domain expert suggests that our approach is a useful addition to the analyst's toolbox for time‐series segmentation.

中文翻译：

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使时间序列分割的参数依赖可视化

这项工作提出了一种方法来支持对时间序列分割的参数依赖性进行可视化分析。目标是帮助分析人员了解哪些参数影响较大，哪些分割属性对参数变化高度敏感。我们的方法首先从分割输出中导出特征，然后计算特征和参数之间的相关性，更准确地说，在参数子范围内，以捕获全局和局部依赖性。专用概览将相关性可视化，以帮助用户了解参数影响并识别参数空间中的不同影响区域。通过在视觉上强调参与相关性的参数范围和段来支持对分段的详细检查。这涉及链接和突出显示，以及一种特殊的排序机制，可以在用户交互式探索各个依赖项时动态调整可视化。该方法应用于分割时间序列以进行活动识别的上下文中。领域专家的非正式反馈表明，我们的方法是对时间序列细分分析师工具箱的有用补充。