Quantile-Based Fuzzy Clustering of Multivariate Time Series in the Frequency Domain
Use este enlace para citar
http://hdl.handle.net/2183/31037
A non ser que se indique outra cousa, a licenza do ítem descríbese como Atribución 4.0 Internacional
Coleccións
- GI-MODES - Artigos [122]
Metadatos
Mostrar o rexistro completo do ítemTítulo
Quantile-Based Fuzzy Clustering of Multivariate Time Series in the Frequency DomainData
2022Cita bibliográfica
LÓPEZ-ORIONA, Ángel, VILAR, José A. and D’URSO, Pierpaolo, 2022. Quantile-based fuzzy clustering of multivariate time series in the frequency domain. Fuzzy Sets and Systems. 2022. Vol. 443, p. 115–154. DOI https://doi.org/10.1016/j.fss.2022.02.015
Resumo
[Abstract] A novel procedure to perform fuzzy clustering of multivariate time series generated from different dependence models is proposed. Different amounts of dissimilarity between the generating models or changes on the dynamic behaviours over time are some arguments justifying a fuzzy approach, where each series is associated to all the clusters with specific membership levels. Our procedure considers quantile-based cross-spectral features and consists of three stages: (i) each element is characterized by a vector of proper estimates of the quantile cross-spectral densities, (ii) principal component analysis is carried out to capture the main differences reducing the effects of the noise, and (iii) the squared Euclidean distance between the first retained principal components is used to perform clustering through the standard fuzzy C-means and fuzzy C-medoids algorithms. The performance of the proposed approach is evaluated in a broad simulation study where several types of generating processes are considered, including linear, nonlinear and dynamic conditional correlation models. Assessment is done in two different ways: by directly measuring the quality of the resulting fuzzy partition and by taking into account the ability of the technique to determine the overlapping nature of series located equidistant from well-defined clusters. The procedure is compared with the few alternatives suggested in the literature, substantially outperforming all of them whatever the underlying process and the evaluation scheme. Two specific applications involving air quality and financial databases illustrate the usefulness of our approach.
Palabras chave
Multivariate time series
Clustering
Quantile cross-spectral density
Fuzzy C-means
Fuzzy C-medoids
Principal component analysis
Clustering
Quantile cross-spectral density
Fuzzy C-means
Fuzzy C-medoids
Principal component analysis
Descrición
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG
Versión do editor
Dereitos
Atribución 4.0 Internacional