TSdist (version 3.2)

CIDDistance: Complexity-Invariant Distance Measure For Time Series

Description

Computes the dissimilarity between two numeric series of the same length by calculating a correction of the Euclidean distance based on the complexity estimation of the series.

Usage

CIDDistance(x, y)

Arguments

x

Numeric vector containing the first time series.

y

Numeric vector containing the second time series.

Value

• d
  The computed distance between the pair of series.

Details

This is simply a wrapper for the diss.CID function of package TSclust. As such, all the functionalities of the diss.CID function are also available when using this function.

Note: The negative definiteness of this distance measure is not explicitly mentioned in the literature, to the best of our knowledge and so, can not be assured. As such, when using it within kernel based classifiers such as Support Vector Machines or Gaussian Processes (i.e by inserting this distance in the Gaussian RBF kernel) the user should make sure that the obtained Gram matrix is positive semi-definite. More information and references to some solutions to this problem can be found in (Pree et al. 2014).

References

Pablo Montero, José A. Vilar (2014). TSclust: An R Package for Time Series Clustering. Journal of Statistical Software, 62(1), 1-43. URL http://www.jstatsoft.org/v62/i01/.

See Also

To calculate this distance measure using ts, zoo or xts objects see TSDistances. To calculate distance matrices of time series databases using this measure see TSDatabaseDistances.

Examples

# The objects example.series1 and example.series2 are two 
# numeric series of length 100.

data(example.series1)
data(example.series2)

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

help(example.series)

# Calculate the compression based distance between the two series using
# the default parameters. 

CIDDistance(example.series1, example.series2)