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.

Getting started

Package details

AuthorVictor Maus [aut, cre] (<https://orcid.org/0000-0002-7385-4723>)
MaintainerVictor Maus <vwmaus1@gmail.com>
LicenseGPL (>= 3)
Version1.0-1
URL https://github.com/vwmaus/twdtw/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("twdtw")

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twdtw documentation built on Aug. 8, 2023, 9:06 a.m.