get_time_series_freq

This function calculates the most common difference between consecutive time points.
This can be useful for determining the aproximate sampling frequency of the time series data.

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

get_time_series_freq function

<dl><dt>x</dt>
<dd>A data frame including a column called `observations`` with the time series</dd></dl>

Arguments

Compute the Most Common Sampling Frequency across all observations — get_time_series_freq

<dl>

<dt>x</dt>
<dd>A data frame including a column called `observations`` with the time series</dd>

</dl>

get_time_series_freq: Compute the Most Common Sampling Frequency across all observations

Description

Usage

Value

Arguments