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twdtw
View on CRAN: Click
here
Download and install twdtw package within the R console
Install from CRAN:
install.packages("twdtw")
Install from Github:
library("remotes")
install_github("cran/twdtw")
Install by package version:
library("remotes")
install_version("twdtw", "1.0-1")
Attach the package and use:
library("twdtw")
Maintained by
Victor Maus
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-07-13
Latest Update: 2023-08-08
Description:
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.
How to cite:
Victor Maus (2023). twdtw: Time-Weighted Dynamic Time Warping. R package version 1.0-1, https://cran.r-project.org/web/packages/twdtw. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0-0 (2023-07-13 16:10)
Other packages that cited twdtw R package
View twdtw citation profile
Other R packages that twdtw depends,
imports, suggests or enhances
Complete documentation for twdtw
Functions, R codes and Examples using
the twdtw R package
Some associated functions: date_to_numeric_cycle . max_cycle_length . plot_cost_matrix . print.twdtw . twdtw .
Some associated R codes: RcppExports.R . convert_date_to_numeric.R . init.R . plot_cost_matrix.R . twdtw.R . zzz.R . Full twdtw package functions and examples
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