CorDistance: Dissimilarities based on Pearson's correlation

In TSdist: Distance Measures for Time Series Data

Dissimilarities based on Pearson's correlation

Description

Computes two different distance measure based on Pearson's correlation between a pair of numeric time series of the same length.

Usage

CorDistance(x, y, ...)

Arguments

x	Numeric vector containing the first time series.
y	Numeric vector containing the second time series.
...	Additional parameters for the function. See diss.COR for more information.

Details

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

Value

d	The computed distance between the pair of series.

Author(s)

Usue Mori, Alexander Mendiburu, Jose A. Lozano.

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/.

Golay, X., Kollias, S., Stoll, G., Meier, D., Valavanis, A., & Boesiger, P. (1998). A new correlation-based fuzzy logic clustering algorithm for FMRI. Magnetic Resonance in Medicine, 40(2), 249–260.

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 first correlation based distance between the series.

CorDistance(example.series1, example.series2)

# Calculate the second correlation based distance between the series
# by specifying \eqn{beta}.

CorDistance(example.series1, example.series2, beta=2)

TSdist documentation built on Aug. 31, 2022, 5:09 p.m.