Find temporal patterns associated to a set of time series

This function takes a set of time series samples as input estimates a set of patterns. The patterns are calculated using a GAM model. The idea is to use a formula of type y ~ s(x), where x is a temporal reference and y if the value of the signal. For each time, there will be as many predictions as there are sample values. The GAM model predicts a suitable approximation that fits the assumptions of the statistical model, based on a smooth function.

This method is based on the "createPatterns" method of the R dtwSat package, which is also described in the reference paper.

Usage

sits_patterns(data = NULL, freq = 8L, formula = y ~ s(x), ...)

Arguments

data: Time series.
freq: Interval in days for estimates.
formula: Formula to be applied in the estimate.
...: Any additional parameters.

Value

Time series with patterns.

References

Maus V, Camara G, Cartaxo R, Sanchez A, Ramos F, Queiroz GR. A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8):3729-3739, August 2016. ISSN 1939-1404. doi:10.1109/JSTARS.2016.2517118.

Maus, V., Câmara, G., Appel, M., & Pebesma, E. (2019). dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R. Journal of Statistical Software, 88(5), 1–31. doi:10.18637/jss.v088.i05 .

Author

Victor Maus, vwmaus1@gmail.com

Gilberto Camara, gilberto.camara@inpe.br

Rolf Simoes, rolfsimoes@gmail.com

Examples

if (sits_run_examples()) {
    patterns <- sits_patterns(cerrado_2classes)
    plot(patterns)
}