Scalar attributes and time series combining patterns for multivariate time series classification

György Fekete, Habil. Andras Molnar
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Abstract

In this paper, we propose new patterns which combine attribute and time series data for classifications to soften or solve the overlapping classes problem. The solution was created for medical human gait analysis, where both types of input data are available.
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标量属性与时间序列相结合的多变量时间序列分类模式
本文提出了一种结合属性数据和时间序列数据进行分类的新模式,以缓解或解决类重叠问题。该解决方案是为医学人类步态分析而创建的,其中两种类型的输入数据都是可用的。
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