Quantile autocovariances: a powerful tool for hard and soft partitional clustering of time series

DOI10.1016/j.fss.2017.03.006zbMath1397.62233OpenAlexW2596274502MaRDI QIDQ1795021

Pierpaolo D'Urso, Borja Lafuente-Rego, José Antonio Vilar

Publication date: 16 October 2018

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11573/973426

zbMATH Keywords

time series clusteringpartitioning around medoidsfuzzy \(C\)-medoids clusteringheteroskedastic modelsquantile autocovariances

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Multivariate analysis and fuzziness (62H86)

Related Items

Temporal gap statistic: a new internal index to validate time series clustering, Robust fuzzy clustering of multivariate time trajectories, Incremental fuzzy clustering of time series, OWA-based robust fuzzy clustering of time series with typicality degrees, Robust fuzzy clustering of time series based on B-splines, Cophenetic-based fuzzy clustering of time series by linear dependency, Trimmed fuzzy clustering of financial time series based on dynamic time warping, Robust fuzzy clustering based on quantile autocovariances, Regime dependent interconnectedness among fuzzy clusters of financial time series, Hierarchical time series clustering on tail dependence with linkage based on a multivariate copula approach, Clustering time series by linear dependency, Quantile-based fuzzy \(C\)-means clustering of multivariate time series: robust techniques

Uses Software

• Tsclust
• quantilogram
• clusfind

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