Fuzzy clustering of time series using extremes
Fuzzy clustering of time series using extremes
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DOI：
10.1016/j.fss.2016.10.006
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发表时间：
2017-07-01
影响因子：
3.9
通讯作者：
Alonso, Andres M.
中科院分区：
文献类型：
--
作者：
D'Urso, Pierpaolo;Maharaj, Elizabeth A.;Alonso, Andres M.
在这项研究中，我们探讨了分组的时间序列相似的季节性模式，使用极值分析与模糊聚类。输入功能到模糊聚类方法的参数估计的时间变化的位置，规模和形状从拟合的广义极值（GEV）分布每年的最大值或r-最大的顺序统计每年的时间序列。这项研究的一个创新贡献是考虑到权重的新的广义模糊聚类程序的开发，以及GEV参数估计的基础上迭代求解的推导。仿真研究进行评估的方法，显示良好的性能。一组每日海平面时间序列的应用程序是从澳大利亚海岸周围，确定的集群得到很好的验证，他们可以有意义的解释。(C)© 2016 Elsevier B. V.版权所有。
In this study we explore the grouping together of time series with similar seasonal patterns using extreme value analysis with fuzzy clustering. Input features into the fuzzy clustering methods are parameter estimates of time varying location, scale and shape obtained from fitting the generalised extreme value (GEV) distribution to annual maxima or the r-largest order statistics per year of the time series. An innovative contribution of the study is the development of new generalised fuzzy clustering procedures taking into account weights, and the derivation of iterative solutions based on the GEV parameter estimators. Simulation studies conducted to evaluate the methods, reveal good performance. An application is made to a set of daily sea-level time series from around the coast of Australia where the identified clusters are well validated and they can be meaningfully interpreted. (C) 2016 Elsevier B.V. All rights reserved.