sits_TS_distances: Calculate a set of distance measures for satellite image time...
In luizassis/sits: Satellite Image Time Series Analysis

Description Usage Arguments Value Author(s)

This function allows the user to select different alternatives to define a set of distances between a set of satellite image time series and a set of patterns. The following alternatives are available in the TSdist package: "euclidean": Euclidean distance. "manhattan": Manhattan distance. "minkowski": Minkowski distance. "infnorm": Infinite norm distance. "ccor": Distance based on the cross-correlation. "sts": Short time series distance. "dtw": Dynamic Time Warping distance. Uses the dtw package (see dtw). "lb.keogh": LB_Keogh lower bound for the Dynamic Time Warping distance. "edr": Edit distance for real sequences. "erp": Edit distance with real penalty. "lcss": Longest Common Subsequence Matching. "fourier": Distance based on the Fourier Discrete Transform. "tquest": TQuest distance. "dissim": Dissim distance. "acf": Autocorrelation-based dissimilarity, Uses the TSclust package (see diss.ACF). "pacf": Partial autocorrelation-based dissimilarity. Uses the TSclust package (see diss.PACF). "ar.lpc.ceps": Dissimilarity based on LPC cepstral coefficients. Uses the TSclust package (see diss.AR.LPC.CEPS). "ar.mah": Model-based dissimilarity proposed by Maharaj (1996, 2000). Uses the TSclust package (see diss.AR.MAH). "ar.pic": Model-based dissimilarity measure proposed by Piccolo (1990). Uses the TSclust package (see diss.AR.PIC). "cdm": Compression-based dissimilarity measure. Uses the TSclust package (see diss.CDM). "cid": Complexity-invariant distance measure. Uses the TSclust package (see diss.CID). "cor": Dissimilarities based on Pearson's correlation. Uses the TSclust package (see diss.COR). "cort": Dissimilarity index which combines temporal correlation and raw value behavior. Uses the TSclust package (see diss.CORT). "wav": Dissimilarity based on wavelet feature extraction. Uses the TSclust package (see diss.DWT). "int.per": Integrated periodogram based dissimilarity IntPerDistance. Uses the TSclust package (see diss.INT.PER). "per": Periodogram based dissimilarity PerDistance. Uses the TSclust package (see diss.PER). "mindist.sax": Symbolic Aggregate Aproximation based dissimilarity. Uses the TSclust package (see diss.MINDIST.SAX). "ncd": Normalized compression based distance NCDDistance. Uses the TSclust package (see diss.NCD). "pred": Dissimilarity measure cased on nonparametric forecasts PredDistance. Uses the TSclust package (see diss.PRED). "spec.glk": Dissimilarity based on the generalized likelihood ratio test. Uses the TSclust package (see diss.SPEC.GLK). "spec.isd": Dissimilarity based on the integrated squared difference between the log-spectra. Uses the TSclust package (see diss.SPEC.ISD). "spec.llr": General spectral dissimilarity measure using local-linear estimation of the logspectra. Uses the TSclust package (see diss.SPEC.LLR). "pdc": Permutation Distribution Distance. Uses the pdc package (see pdcDist). "frechet": Frechet distance. Uses the longitudinalData package (see distFrechet).

1 2	sits_TS_distances(data.tb = NULL, patterns.tb = NULL, bands = NULL, distance = "dtw", ...)

`data.tb`	a SITS tibble time series
`patterns.tb`	a set of patterns obtained from training samples
`bands`	the bands to be used for determining patterns
`distance`	a method for calculating distances between time series
`...`	Additional parameters required by the distance method.