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asardaes
dtwclust:Time Series Clustering Along with Optimizations for the Dynamic Time Warping Distance
Time series clustering along with optimized techniques related to the Dynamic Time Warping distance and its corresponding lower bounds. Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions. Implementations of DTW barycenter averaging, a distance based on global alignment kernels, and the soft-DTW distance and centroid routines are also provided. All included distance functions have custom loops optimized for the calculation of cross-distance matrices, including parallelization support. Several cluster validity indices are included.
Maintained by Alexis Sarda. Last updated 8 months ago.
clusteringdtwtime-seriesopenblascpp
262 stars 12.35 score 406 scripts 14 dependents