dtw-package: Comprehensive implementation of Dynamic Time Warping (DTW)...

Description Details Author(s) References See Also Examples

Description

The DTW algorithm computes the stretch of the time axis which optimally maps one given timeseries (query) onto whole or part of another (reference). It yields the remaining cumulative distance after the alignment and the point-by-point correspondence (warping function). DTW is widely used e.g. for classification and clustering tasks in econometrics, chemometrics and general timeseries mining.

Details

Please see documentation for function dtw(), which is the main entry point to the package.

The R implementation in dtw provides:

Multivariate timeseries can be aligned with arbitrary local distance definitions, leveraging the proxy::dist() function of package proxy. DTW itself becomes a distance function with the dist semantics.

In addition to computing alignments, the package provides:

If you use this software, please cite it according to citation("dtw"). The package home page is at https://dynamictimewarping.github.io.

Author(s)

Toni Giorgino

References

See Also

dtw() for the main entry point to the package; dtwWindowingFunctions() for global constraints; stepPattern() for local constraints; proxy::dist(), analogue::distance(), vegan::vegdist() to build local cost matrices for multivariate timeseries and custom distance functions.

Examples

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 library(dtw);
 ## demo(dtw);

dtw documentation built on Oct. 23, 2020, 8:11 p.m.