Noise fuzzy clustering of time series by autoregressive metric
Noise fuzzy clustering of time series by autoregressive metric
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DOI：
10.1007/s40300-013-0024-x
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发表时间：
2013-11-01
影响因子：
0.8
通讯作者：
Di Lallo, Dario
中科院分区：
文献类型：
--
作者：
D'Urso, Pierpaolo;De Giovanni, Livia;Di Lallo, Dario
我们提出了一种鲁棒模糊聚类模型用于时间序列的分类，该模型考虑了自回归度量。特别是，我们提出了一种聚类过程：1)考虑时间序列的自回归参数化，能够表示大量的时间序列；2)继承了围绕中间点划分方法的优点，将时间序列划分为以原型观测时间序列（“中间点”时间序列）为特征的类，综合了每个聚类的结构信息；3)继承模糊方法的优点，捕捉特定时间序列的模糊（模糊）行为，例如“中间”时间序列（在所有时间段内所考虑的聚类具有中间特征的时间序列）和“切换”时间序列（在特定时间段内具有给定聚类的典型模式的时间序列，在另一个时间段具有与另一个聚类相似的完全不同的模式）；4)能够适当地抵消聚类过程中“离群值”时间序列存在的负面影响，即“离群值”时间序列被分类在所谓的“噪声聚类”中，因此聚类结构不会改变。为了说明该模型的有效性，进行了仿真研究并应用于实时序列。
We propose a robust fuzzy clustering model for classifying time series, considering the autoregressive metric based. In particular, we suggest a clustering procedure which: 1) considers an autoregressive parameterization of the time series, capable of representing a large class of time series; 2) inherits the benefits of the partitioning around medoids approach, classifying time series in classes characterized by prototypal observed time series (the "medoid" time series), which synthesize the structural information of each cluster; 3) inherits the benefits of the fuzzy approach, capturing the vague (fuzzy) behaviour of particular time series, such as "middle" time series (time series with middle features in respect of the considered clusters in all time period) and "switching" time series (time series with a pattern typical of a given cluster during a certain time period and a completely different pattern, similar to another cluster, in another time period); 4) is capable of suitably neutralizing the negative influence of the presence of "outlier" time series in the clustering procedure, i.e., the "outlier" time series are classified in the so-called "noise cluster" and therefore cluster structure is not altered. To illustrate the effectiveness of the proposed model, a simulation study and an application to real time series are carried out.