Comparer des méthodes

	Moyennage barycentrique DTW	Regroupement hiérarchique
Domaine	Séries temporelles	Apprentissage automatique
Famille	Process / pipeline	Machine learning
Année d'origine	2011	1963
Auteur d'origine	François Petitjean	Ward, J. H.
Type	Distance-based time-series aggregation	Unsupervised clustering (agglomerative)
Source fondatrice	Salvador, S., & Chan, P. (2004). FastDTW: Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis, 11(5), 561–580.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Alias	DBA, DTW-BA, Barycenter Averaging	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Apparentées	4	4
Résumé	DTW Barycenter Averaging (DBA) is a method for computing the average or representative sequence of a set of time series that respects temporal warping and elastic distance. Unlike Euclidean averaging which requires point-wise alignment, DBA minimizes the sum of Dynamic Time Warping (DTW) distances, producing a meaningful average for sequences with flexible temporal alignments. Introduced by Petitjean and colleagues in 2011, it is widely used in time-series clustering and summarization.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.