Classification of Time Series Data with Nonlinear Similarity Measures
Classification of Time Series Data with Nonlinear Similarity Measures
复制标题
使用非线性相似性度量对时间序列数据进行分类
DOI：
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发表时间：
1997
期刊：
影响因子：
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通讯作者：
Andreas Schmitz
中科院分区：
文献类型：
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作者：
T. Schreiber;Andreas Schmitz
We address the problem of unsupervised classification of time series recordings. The aim is to form groups of sequences or segments of a longer sequence which show similar dynamical behavior. Several measures of similarity between two time series are considered. Groups or clusters are then formed by minimizing a suitable cost function using simulated annealing. Apart from the classification of systems, the method can be applied to the monitoring of abrupt or continuous changes in nonstationary signals. We further discuss the inclusion of supervision and propose the use of similarity measures in tests for nonlinearity based on surrogate data.
