Package: twdtw 1.0-1
Victor Maus
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.
Authors:
twdtw_1.0-1.tar.gz
twdtw_1.0-1.tar.gz(r-4.5-noble)twdtw_1.0-1.tar.gz(r-4.4-noble)
twdtw_1.0-1.tgz(r-4.4-emscripten)
twdtw.pdf |twdtw.html✨
twdtw/json (API)
NEWS
| # Install 'twdtw' in R: |
| install.packages('twdtw', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/vwmaus/twdtw/issues
Last updated 1 years agofrom:c6e659f785. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 06 2024 |
| R-4.5-linux-x86_64 | OK | Dec 06 2024 |
Exports:date_to_numeric_cyclemax_cycle_lengthplot_cost_matrixtwdtw
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Convert Date/POSIXct to a Numeric Cycle | date_to_numeric_cycle |
| Calculate the Maximum Possible Value of a Time Cycle | max_cycle_length |
| Plot TWDTW cost matrix | plot_cost_matrix |
| Print method for twdtw class | print.twdtw |
| Calculate Time-Weighted Dynamic Time Warping (TWDTW) distance | twdtw twdtw.data.frame twdtw.matrix |
