TSdist (version 1.2)

tsDistances: 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", "infinitenorm", "pearsoncorrelation", "crosscorrelation", "sts", "dtw"<

...

Additional parameters required by the distance method.

Value

• d
  The computed distance between the pair of time series.

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
• "infinitenorm": Infinite norm distance.infiniteNormDistance
• "pearsoncorrelation": Distances based on Pearson's correlation.correlationDistance
• "crosscorrelation": Distances based on the cross-correlation.crossCorrelationDistance
• "sts": Short time series distance.stsDistance
• "dtw": Dynamic Time Warping distance.dtwDistance
• "keogh_lb": 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
• "dissimapprox": Approximated Dissim distance.dissimapproxDistance

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

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="infinitenorm")
tsDistances(zoo.series1, zoo.series2, distance="pearsoncorrelation", beta=3)
tsDistances(zoo.series1, zoo.series2, distance="dtw", sigma=20)