R: TSdist distance computation.

TSDistances {TSdist} | R Documentation

## TSdist distance computation.

### Description

TSdist distance calculation between two time series.

### Usage

    TSDistances(x, y, tx, ty, distance, ...)

### Arguments

`x` | Numeric vector or `ts`, `zoo` or `xts` object containing the first time series.
`y` | Numeric vector or `ts`, `zoo` or `xts` object containing the second time series.
`tx` | Optional temporal index of series `x`. Only necessary if `x` is a numeric vector and the sampling index is not constant.
`ty` | Optional temporal index of series `y`. Only necessary if `y` is a numeric vector and the sampling index is not constant.
`distance` | Distance measure to be used. It must be one of: `"euclidean"`, `"manhattan"`, `"minkowski"`, `"infnorm"`, `"ccor"`, `"sts"`, `"dtw"`, `"keogh.lb"`, `"edr"`, `"erp"`, `"lcss"`, `"fourier"`, `"tquest"`, `"dissim"`, `"acf"`, `"pacf"`, `"ar.lpc.ceps"`, `"ar.mah"`, `"ar.mah.statistic"`, `"ar.mah.pvalue"`, `"ar.pic"`, `"cdm"`, `"cid"`, `"cor"`, `"cort"`, `"wav"`, `"int.per"`, `"per"`, `"mindist.sax"`, `"ncd"`, `"pred"`, `"spec.glk"`, `"spec.isd"`, `"spec.llr"`, `"pdc"`, `"frechet"`,`"tam"`)
`...` | Additional parameters required by the distance method.

### Details

The distance between the two time series `x` and `y` is calculated. `x` and `y` can be saved in a numeric vector or a `ts`, `zoo` or `xts` object. The following distance methods are supported:

- "euclidean": Euclidean distance. `EuclideanDistance`

- "manhattan": Manhattan distance. `ManhattanDistance`

- "minkowski": Minkowski distance. `MinkowskiDistance`

- "infnorm": Infinite norm distance. `InfNormDistance`

- "ccor": Distance based on the cross-correlation. `CCorDistance`

- "sts": Short time series distance. `STSDistance`

- "dtw": Dynamic Time Warping distance. `DTWDistance`. Uses the dtw package (see `dtw`).

- "lb.keogh": LB_Keogh lower bound for the Dynamic Time Warping distance. `LBKeoghDistance`

- "edr": Edit distance for real sequences. `EDRDistance`

- "erp": Edit distance with real penalty. `ERPDistance`

- "lcss": Longest Common Subsequence Matching. `LCSSDistance`

- "fourier": Distance based on the Fourier Discrete Transform. `FourierDistance`

- "tquest": TQuest distance. `TquestDistance`

- "dissim": Dissim distance. `DissimDistance`

- "acf": Autocorrelation-based dissimilarity `ACFDistance`. Uses the TSclust package (see `diss.ACF`).

- "pacf": Partial autocorrelation-based dissimilarity `PACFDistance`. Uses the TSclust package (see `diss.PACF`).

- "ar.lpc.ceps": Dissimilarity based on LPC cepstral coefficients `ARLPCCepsDistance`. Uses the TSclust package (see `diss.AR.LPC.CEPS`).

- "ar.mah": Model-based dissimilarity proposed by Maharaj (1996, 2000) `ARMahDistance`. Uses the TSclust package (see `diss.AR.MAH`).

- "ar.pic": Model-based dissimilarity measure proposed by Piccolo (1990) `ARPicDistance`. Uses the TSclust package (see `diss.AR.PIC`).

- "cdm": Compression-based dissimilarity measure `CDMDistance`. Uses the TSclust package (see `diss.CDM`).

- "cid": Complexity-invariant distance measure `CIDDistance`. Uses the TSclust package (see `diss.CID`).

- "cor": Dissimilarities based on Pearson's correlation `CorDistance`. Uses the TSclust package (see `diss.COR`).

- "cort": Dissimilarity index which combines temporal correlation and raw value behaviors `CortDistance`. Uses the TSclust package (see `diss.CORT`).

- "wav": Dissimilarity based on wavelet feature extraction `WavDistance`. 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 `MindistSaxDistance`. 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 `SpecGLKDistance`. Uses the TSclust package (see `diss.SPEC.GLK`).

- "spec.isd": Dissimilarity based on the integrated squared difference between the log-spectra `SpecISDDistance`. Uses the TSclust package (see `diss.SPEC.ISD`).

- "spec.llr": General spectral dissimilarity measure using local-linear estimation of the log-spectra `SpecLLRDistance`. Uses the TSclust package (see `diss.SPEC.LLR`).

- "pdc": Permutation Distribution Distance `PDCDistance`. Uses the pdc package (see `pdcDist`).

- "frechet": Frechet distance `FrechetDistance`. Uses the longitudinalData package (see `distFrechet`).

- "tam": Time Aligment Measurement `TAMDistance`.

Some distance measures may require additional arguments. See the individual help pages (detailed above) for more information about each method.

### Value

`d` | The computed distance between the pair of time series.

### Author(s)

Usue Mori, Alexander Mendiburu, Jose A. Lozano.

### Examples

    # The objects zoo.series1 and zoo.series2 are two
    # zoo objects that save two series of length 100.

    data(zoo.series1)
    data(zoo.series2)

    # For information on their generation and shape see
    # help page of example.series.

    help(example.series)

    # The distance calculation for these two series is done
    # as follows:

    TSDistances(zoo.series1, zoo.series2, distance="infnorm")
    TSDistances(zoo.series1, zoo.series2, distance="cor", beta=3)
    TSDistances(zoo.series1, zoo.series2, distance="dtw", sigma=20)

[Package TSdist version 3.6 Index]