# diss.COR

0th

Percentile

##### Correlation-based Dissimilarity

Computes dissimilarities based on the estimated Pearson's correlation of two given time series.

Keywords
~kwd1 , ~kwd2
##### Usage
diss.COR(x, y, beta = NULL)
##### Arguments
x

Numeric vector containing the first of the two time series.

y

Numeric vector containing the second of the two time series.

beta

If not NULL, specifies the regulation of the convergence in the second method.

##### Details

Two different measures of dissimilarity between two time series based on the estimated Pearson's correlation can be computed. If beta is not specified, the value $d_1 = \sqrt{ 2 ( 1 - \rho) }$ is computed, where $(\rho)$ denotes the Pearson's correlation between series x and y. If beta is specified, the function $d_2 = \sqrt{ (\frac{ 1 - \rho}{ 1 + \rho})^\beta }$ is used, where $\beta$ is beta .

##### Value

The computed distance.

##### References

Golay, X., Kollias, S., Stoll, G., Meier, D., Valavanis, A., and Boesiger, P. (2005) A new correlation-based fuzzy logic clustering algorithm for FMRI. Magnetic Resonance in Medicine, 40.2, 249--260.

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

diss.PACF, diss.ACF, diss

• diss.COR
##### Examples
# NOT RUN {
## Create three sample time series
x <- cumsum(rnorm(100))
y <- cumsum(rnorm(100))
z <- sin(seq(0, pi, length.out=100))
## Compute the distance and check for coherent results
diss.COR(x, y)
diss.COR(x, z)
#create a dist object for its use with clustering functions like pam or hclust
# }
# NOT RUN {
diss( rbind(x,y,z), "COR")
# }

Documentation reproduced from package TSclust, version 1.2.4, License: GPL-2

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