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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：当某些感兴趣变量的实际间隔被注册为沿时间的有序序列时，发生间隔时间序列。我们解决了聚类间隔时间序列（其）的问题，提出了不同方法。首先，基于点对点比较执行群集。时域和小波特征也用作替代方法中的聚类变量。此外，可以使用足够的距离（例如frobenius距离）来比较自相关矩阵函数，收集其上限和下限的自相关和互相关功能，并用于聚类其。提出了一种确定其自相关函数的改进过程，其也是聚类的基础。与其在不同设置下的模拟相比，探索了不同的替代方法及其性能。在澳大利亚不同地点观察到海平面每日范围的应用，说明了所提出的方法。

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