Categorizing Uncertainties in the Process of Segmenting and Labeling Time Series Data
Categorizing Uncertainties in the Process of Segmenting and Labeling Time Series Data
复制标题
对时间序列数据分段和标记过程中的不确定性进行分类
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
S. Miksch
中科院分区：
文献类型：
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作者：
M. Bögl;C. Bors;T. Gschwandtner;S. Miksch
多变量时间序列数据的分割和标注应用于不同的领域，如活动识别或传感器状态。这涉及时间间隔的(预)处理、分段和标记的几个步骤，以及可视地探索结果以及迭代地精炼所有处理步骤的参数。在这些进程中，不同的不确定因素相互关联。在这张海报中，我们对这个问题领域中的重要不确定性进行了识别和分类。我们讨论了在整个细分和标签过程中视觉传达这些不确定性的挑战。CCS概念·以人为中心的计算→可视化理论、概念和范例；·计算→时间序列分析的数学；
The segmenting and labeling of multivariate time series data is applied in different domains, e.g. activity recognition or sensor states. This involves several steps of (pre-) processing, segmenting, and labeling of time intervals, and visually exploring the results as well as iteratively refining the parameters for all the processing steps. Within these processes different uncertainties are involved and relevant. In this poster we identify and categorize important uncertainties in this problem domain. We discuss challenges for visually communicating these uncertainties throughout the segmenting and labeling process. CCS Concepts •Human-centered computing → Visualization theory, concepts and paradigms; •Mathematics of computing → Time series analysis;