shift_dates

Shifts a vector of dates to start on the same day-of-year in a specified origin year
while preserving the relative difference in days among the observations.
This way the temporal pattern (e.g., seasonality) inherent to the original dates
will also be preserved in the shifted dates.

Provides a robust approach to land use mapping using multi-dimensional
(multi-band) satellite image time series. By leveraging the Time-Weighted Dynamic
Time Warping (TWDTW) distance metric in tandem with a 1 Nearest-Neighbor (1-NN) Classifier,
this package offers functions to produce land use maps based on distinct seasonality patterns,
commonly observed in the phenological cycles of vegetation. The approach is described in
Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and Maus et al. (2019) <doi:10.18637/jss.v088.i05>.
A primary advantage of TWDTW is its capability to handle irregularly sampled and noisy time series,
while also requiring minimal training sets. The package includes tools for training the 1-NN-TWDTW model,
visualizing temporal patterns, producing land use maps, and visualizing the results.

Victor Maus

dtwSat

Time-Weighted Dynamic Time Warping for Satellite Image Time
Series Analysis

Marius Appel

Nikolas Kuschnig

Toni Giorgino

shift_dates function

<dl><dt>x</dt>
<dd>A vector of date strings or Date objects representing the sequence to shift.</dd>
<dt>origin</dt>
<dd>A date string or Date object specifying the desired origin year for the shifted dates.
Default is "1970-01-01".</dd></dl>

Arguments

Shift Dates to Start on a Specified Origin Year — shift_dates

<dl>

<dt>x</dt>
<dd>A vector of date strings or Date objects representing the sequence to shift.</dd>


<dt>origin</dt>
<dd>A date string or Date object specifying the desired origin year for the shifted dates.
Default is "1970-01-01".</dd>

</dl>

shift_dates: Shift Dates to Start on a Specified Origin Year

Description

Usage

Value

Arguments

Details

Examples