Package: dtwclust 6.0.0.9000

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.

Authors:Alexis Sarda-Espinosa

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dtwclust.pdf |dtwclust.html✨
dtwclust/json (API)
NEWS

# Install 'dtwclust' in R:
install.packages('dtwclust', repos = c('https://asardaes.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/asardaes/dtwclust/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

clusteringdtwtime-series

12.43 score 258 stars 14 packages 490 scripts 4.9k downloads 11 mentions 41 exports 75 dependencies

Last updated 4 months agofrom:9efcdb04d6. Checks:OK: 3 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-win-x86_64OKNov 20 2024
R-4.5-linux-x86_64OKNov 20 2024
R-4.4-win-x86_64NOTENov 20 2024
R-4.4-mac-x86_64NOTENov 20 2024
R-4.4-mac-aarch64NOTENov 20 2024
R-4.3-win-x86_64NOTENov 20 2024
R-4.3-mac-x86_64NOTENov 20 2024
R-4.3-mac-aarch64NOTENov 20 2024

Exports:as.matrixcompare_clusteringscompare_clusterings_configscompute_envelopecvicvi_evaluatorsdbaDBAdtw_basicdtw_lbdtw2fuzzy_controlgakGAKhierarchical_controlinteractive_clusteringlb_improvedlb_keoghNCCcpam_centpartitional_controlpdc_configsplotpredictreinterpolaterepeat_clusteringsbdSBDsdtwsdtw_centshape_extractionshowssdtwclusttadpoleTADPoletadpole_controltsclusttsclust_argstslistupdatezscore

Dependencies:base64encbslibcachemclasscliclueclustercodetoolscolorspacecommonmarkcrayondigestdplyrdtwfansifarverfastmapflexclustfontawesomeforeachfsgenericsggplot2ggrepelgluegtablehtmltoolshttpuvisobanditeratorsjquerylibjsonlitelabelinglaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemodeltoolsmunsellnlmepillarpkgconfigplyrpromisesproxyR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRcppThreadreshape2rlangRSpectrasassscalesshinyshinyjssourcetoolsstringistringrtibbletidyselectutf8vctrsviridisLitewithrxtable

Comparing Time-Series Clustering Algorithms in R Using the dtwclust Package

Rendered fromdtwclust.Rnwusingknitr::knitr_notangleon Nov 20 2024.

Last update: 2019-09-18
Started: 2016-08-29

Parallelization considerations for dtwclust

Alexis Sarda-Espinosa

Rendered fromparallelization-considerations.Rmdusingknitr::rmarkdown_notangleon Nov 20 2024.

Last update: 2019-06-29
Started: 2018-01-20

Timing experiments for dtwclust

Alexis Sarda-Espinosa

Rendered fromtiming-experiments.Rmdusingknitr::rmarkdown_notangleon Nov 20 2024.

Last update: 2019-05-03
Started: 2017-07-29

Readme and manuals

Help Manual

Help pageTopics
Time series clustering along with optimizations for the Dynamic Time Warping distancedtwclust-package dtwclust
as.matrixas.matrix
Compare different clustering configurationscompare_clusterings
Create clustering configurations.compare_clusterings_configs
Time series warping envelopescompute_envelope
Cluster validity indicescvi cvi,FuzzyTSClusters cvi,FuzzyTSClusters-method cvi,HierarchicalTSClusters cvi,HierarchicalTSClusters-method cvi,matrix-method cvi,PartitionalTSClusters cvi,PartitionalTSClusters-method
Cluster comparison based on CVIscvi_evaluators
DTW Barycenter AveragingDBA dba
Distance matrix's lower triangularDistmatLowerTriangular DistmatLowerTriangular-class
Basic DTW distancedtw_basic
DTW distance matrix guided by Lemire's improved lower bounddtw_lb
DTW distance with L2 normdtw2
Results of timing experimentsdtwclustTimings
Fast global alignment kernelsGAK gak
A shiny app for interactive clusteringinteractive_clustering
Lemire's improved DTW lower boundlb_improved
Keogh's DTW lower boundlb_keogh
Cross-correlation with coefficient normalizationNCCc
Centroid for partition around medoidspam_cent
Helper function for preprocessing/distance/centroid configurationspdc_configs
Wrapper for simple linear reinterpolationreinterpolate
Repeat a clustering configurationrepeat_clustering
Shape-based distanceSBD sbd
Soft-DTW distancesdtw
Centroid calculation based on soft-DTWsdtw_cent
Shape average of several time seriesshape_extraction
A shiny app for semi-supervised DTW-based clusteringssdtwclust
TADPole clusteringTADPole tadpole
Time series clusteringtsclust
Control parameters for clusterings with 'tsclust()'fuzzy_control hierarchical_control partitional_control tadpole_control tsclust-controls tsclust_args
Class definition for 'TSClusters' and derived classesFuzzyTSClusters FuzzyTSClusters-class HierarchicalTSClusters HierarchicalTSClusters-class PartitionalTSClusters PartitionalTSClusters-class TSClusters TSClusters-class
Methods for 'TSClusters'initialize,TSClusters initialize,TSClusters-method plot,TSClusters,missing plot,TSClusters,missing-method plot.TSClusters predict,TSClusters predict,TSClusters-method predict.TSClusters show,TSClusters show,TSClusters-method TSClusters-methods tsclusters-methods update,TSClusters update,TSClusters-method update.TSClusters
Class definition for 'tsclustFamily'tsclustFamily tsclustFamily-class
Coerce matrices or data frames to a list of time seriestslist
Subset of character trajectories data setCharTraj CharTrajLabels CharTrajMV uciCT ucict
Wrapper for z-normalizationzscore