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.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("dtwclust")

6.0.0 by Alexis Sarda, 2 years ago


https://github.com/asardaes/dtwclust


Report a bug at https://github.com/asardaes/dtwclust/issues


Browse source code at https://github.com/cran/dtwclust


Authors: Alexis Sarda-Espinosa


Documentation:   PDF Manual  


GPL-3 license


Imports parallel, stats, utils, clue, cluster, dplyr, flexclust, foreach, ggplot2, ggrepel, rlang, Matrix, RSpectra, Rcpp, RcppParallel, reshape2, shiny, shinyjs

Depends on methods, proxy, dtw

Suggests doParallel, iterators, knitr, rmarkdown, testthat

Linking to Rcpp, RcppArmadillo, RcppParallel, RcppThread

System requirements: GNU make


Imported by WOAkMedoids, ddc, harbinger, sensitivity.

Suggested by IncDTW, latrend, regional, sits.


See at CRAN