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.
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