A key issue in cluster analysis is determining a proper dissimilarity measure between two data objects, and many pairwise dissimilarities have been proposed to deal with time series. Assuming that the clustering purpose is to group series according to the underlying dependence structures, a detailed study of the behavior in clustering of a dissimilarity based on comparing estimated quantile autocovariance functions (QAF) is carried out. Quantile autocovariances provide information about the serial dependence structure that other conventional features are not able to capture, which suggests great potential to perform clustering of series. The asymptotic behavior of the sample quantile autocovariances is studied and an algorithm to deter...
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Clustering of m...
The detection of patterns in multivariate time series is a relevant task, especially for large datas...
A new cluster analysis method, K-quantiles clustering, is introduced. K-quantiles clustering can be ...
A novel procedure to perform fuzzy clustering of multivariate time series generated from different d...
Robust fuzzy clustering of multivariate time series is addressed when the clustering purpose is grou...
Robustness to the presence of outliers in time series clustering is addressed. Assuming that the clu...
In this article, a fuzzy clustering model for multivariate time series based on the quantile cross-s...
The traditional approaches to clustering a set of time series are generally applicable if there is a...
In this paper, following a fuzzy approach and adopting an autoregressive parameterization, we propos...
We propose a robust fuzzy clustering model for classifying time series, considering the autoregressi...
In this paper, following a fuzzy approach and adopting an autoregressive parameterization, we propos...
Abstract: Clustering algorithms have been actively used to identify similar time series, providing a...
In this paper, we propose two important measures, quantile correlation (QCOR) and quantile partial c...
<div><p>In this article, we propose two important measures, quantile correlation (QCOR) and quantile...
Crisp and fuzzy clustering methods based on a combination of univariate and multivariate wavelet fea...
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Clustering of m...
The detection of patterns in multivariate time series is a relevant task, especially for large datas...
A new cluster analysis method, K-quantiles clustering, is introduced. K-quantiles clustering can be ...
A novel procedure to perform fuzzy clustering of multivariate time series generated from different d...
Robust fuzzy clustering of multivariate time series is addressed when the clustering purpose is grou...
Robustness to the presence of outliers in time series clustering is addressed. Assuming that the clu...
In this article, a fuzzy clustering model for multivariate time series based on the quantile cross-s...
The traditional approaches to clustering a set of time series are generally applicable if there is a...
In this paper, following a fuzzy approach and adopting an autoregressive parameterization, we propos...
We propose a robust fuzzy clustering model for classifying time series, considering the autoregressi...
In this paper, following a fuzzy approach and adopting an autoregressive parameterization, we propos...
Abstract: Clustering algorithms have been actively used to identify similar time series, providing a...
In this paper, we propose two important measures, quantile correlation (QCOR) and quantile partial c...
<div><p>In this article, we propose two important measures, quantile correlation (QCOR) and quantile...
Crisp and fuzzy clustering methods based on a combination of univariate and multivariate wavelet fea...
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Clustering of m...
The detection of patterns in multivariate time series is a relevant task, especially for large datas...
A new cluster analysis method, K-quantiles clustering, is introduced. K-quantiles clustering can be ...