Time Series Clustering Along with Optimizations for the Dynamic Time Warping Distance
Generics for SparseDistmat
A shiny app for semi-supervised DTW-based clustering
TADPole clustering
Control parameters for clusterings with tsclust()
Time series clustering
This helper will produce the plot in the Explore tab panel.
as.matrix
Create clustering configurations.
Compare different clustering configurations
Time series warping envelopes
This helper will create the data frame used to plot in the Explore tab...
Fast global alignment kernels
Cluster comparison based on CVIs
Cluster validity indices
DTW Barycenter Averaging
Distance matrix
Generics for Distmat
Distance matrix's lower triangular
Generics for DistmatLowerTriangular
Basic DTW distance
DTW distance matrix guided by Lemire's improved lower bound
DTW distance with L2 norm
Time series clustering along with optimizations for the Dynamic Time W...
A shiny app for interactive clustering
Lemire's improved DTW lower bound
Keogh's DTW lower bound
Cross-correlation with coefficient normalization
Helper for semi-supervised DTW clustering
Centroid for partition around medoids
This helper will parse comma-separated key-value pairs
Helper function for preprocessing/distance/centroid configurations
Wrapper for simple linear reinterpolation
Repeat a clustering configuration
Shape-based distance
Centroid calculation based on soft-DTW
Soft-DTW distance
Shape average of several time series
Sparse distance matrix
Class definition for TSClusters and derived classes
Methods for TSClusters
Class definition for tsclustFamily
Coerce matrices or data frames to a list of time series
Wrapper for z-normalization
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.