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Clustering of interval time series

机译：间隔时间序列的聚类

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摘要

Interval time series occur when real intervals of some variable of interest are registered as an ordered sequence along time. We address the problem of clustering interval time series (ITS), for which different approaches are proposed. First, clustering is performed based on point-to-point comparisons. Time-domain and wavelet features also serve as clustering variables in alternative approaches. Furthermore, autocorrelation matrix functions, gathering the autocorrelation and cross-correlation functions of the ITS upper and lower bounds, may be compared using adequate distances (e.g. the Frobenius distance) and used for clustering ITS. An improved procedure to determine the autocorrelation function of ITS is proposed, which also serves as a basis for clustering. The different alternative approaches are explored and their performances compared for ITS simulated under different setups. An application to sea level daily ranges, observed at different locations in Australia, illustrates the proposed methods.
机译：当某个感兴趣变量的实际间隔沿时间记录为有序序列时，就会出现间隔时间序列。我们解决了聚类间隔时间序列（ITS）的问题，为此提出了不同的方法。首先，基于点对点比较执行聚类。时域和小波特征在替代方法中也充当聚类变量。此外，可以使用足够的距离（例如，弗罗贝尼乌斯距离）比较收集ITS上界和下界的自相关和互相关函数的自相关矩阵函数，并将其用于对ITS进行聚类。提出了一种确定ITS自相关函数的改进方法，该方法也可作为聚类的基础。探索了不同的替代方法，并比较了在不同设置下模拟ITS的性能。在澳大利亚不同地点观察到的海平面日范围的应用说明了所建议的方法。

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