Pattern recognition and classification for multivariate time series
Pattern recognition and classification for multivariate time series
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DOI：
10.1145/2003653.2003657
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发表时间：
2011-08
期刊：
影响因子：
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通讯作者：
S. Spiegel;J. Gaebler;A. Lommatzsch;E. D. De Luca;S. Albayrak
中科院分区：
文献类型：
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作者：
S. Spiegel;J. Gaebler;A. Lommatzsch;E. D. De Luca;S. Albayrak
如今，我们面临着快速增长和不断变化的数据，包括社交网络和从智能手机或车辆记录的传感器数据。时间演化数据给数据挖掘和机器学习社区带来了许多新的挑战。本文关注的是识别多变量时间序列中的重复模式，它捕获了多个参数在一定时间内的演变。我们的方法首先将一个时间序列分成可以被认为是情况的片段，然后将识别的片段聚类成具有相似背景的组。根据各个信号的相关性，以自底向上的方式建立时间序列分割。已识别的段进行分组的统计特征，使用凝聚层次聚类。所提出的方法进行评估的基础上，从不同的车辆在汽车驾驶记录的真实传感器数据。根据我们的评估，它是可行的，以识别重复模式的时间序列由下而上的分割和层次聚类。
Nowadays we are faced with fast growing and permanently evolving data, including social networks and sensor data recorded from smart phones or vehicles. Temporally evolving data brings a lot of new challenges to the data mining and machine learning community. This paper is concerned with the recognition of recurring patterns within multivariate time series, which capture the evolution of multiple parameters over a certain period of time. Our approach first separates a time series into segments that can be considered as situations, and then clusters the recognized segments into groups of similar context. The time series segmentation is established in a bottom-up manner according the correlation of the individual signals. Recognized segments are grouped in terms of statistical features using agglomerative hierarchical clustering. The proposed approach is evaluated on the basis of real-life sensor data from different vehicles recorded during car drives. According to our evaluation it is feasible to recognize recurring patterns in time series by means of bottom-up segmentation and hierarchical clustering.