twdtw: Time-Weighted Dynamic Time Warping

Implements Time-Weighted Dynamic Time Warping (TWDTW), a measure for quantifying time series similarity. The TWDTW algorithm, described in Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and Maus et al. (2019) <doi:10.18637/jss.v088.i05>, is applicable to multi-dimensional time series of various resolutions. It is particularly suitable for comparing time series with seasonality for environmental and ecological data analysis, covering domains such as remote sensing imagery, climate data, hydrology, and animal movement. The 'twdtw' package offers a user-friendly 'R' interface, efficient 'Fortran' routines for TWDTW calculations, flexible time weighting definitions, as well as utilities for time series preprocessing and visualization.

Version: 1.0-1
Imports: Rcpp, proxy
LinkingTo: Rcpp
Suggests: rbenchmark, testthat (≥ 3.0.0)
Published: 2023-08-08
DOI: 10.32614/CRAN.package.twdtw
Author: Victor Maus ORCID iD [aut, cre]
Maintainer: Victor Maus <vwmaus1 at>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: README NEWS
In views: TimeSeries
CRAN checks: twdtw results


Reference manual: twdtw.pdf


Package source: twdtw_1.0-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): twdtw_1.0-1.tgz, r-oldrel (arm64): twdtw_1.0-1.tgz, r-release (x86_64): twdtw_1.0-1.tgz, r-oldrel (x86_64): twdtw_1.0-1.tgz
Old sources: twdtw archive


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