TSdist (version 1.0)

tsDatabaseDistances: TSdist distance matrix computation.

Description

TSdist distance matrix computation for time series databases.

Usage

tsDatabaseDistances(x, method, diag=FALSE, upper=FALSE, ...)

Arguments

x

Time series database saved in a numeric matrix, a list, an mts object, a zoo object or xts object.

method

Distance measure to be used. It must be one of: "euclidean", "manhattan", "minkowski", "infinitenorm", "pearsoncorrelation", "crosscorrelation", "sts", "dtw"<

diag

logical value indicating if the diagonal of the distance matrix is to be printed.

upper

logical value indicating if the upper triangle of the distance matrix is to be printed.

...

Additional parameters required by the chosen distance measure.

Value

• D
  The computed distance matrix of the time series database.

Details

The distance matrix of a time series database is calculated by providing the pair-wise distances between the series that conform it. x can be saved in a numeric matrix, a list or a mts, 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 object example.database is a numeric matrix that saves
#6 ARIMA time series in a row-wise format. For more information
#see help page of example.databases:

help(example.database)
data(example.database)

#To calculate the distance matrix of this database: 

tsDatabaseDistances(example.database, method="manhattan")
tsDatabaseDistances(example.database, method="edr", epsilon=0.2)
tsDatabaseDistances(example.database, method="fourier", n=20)

#The object zoo.database is a zoo object that saves
#the same 6 ARIMA time series saved in example.database. 

data(zoo.database)

#To calculate the distance matrix of this database: 

tsDatabaseDistances(zoo.database, method="manhattan")
tsDatabaseDistances(zoo.database, method="edr", epsilon=0.2)
tsDatabaseDistances(zoo.database, method="fourier", n=20)