# diss.COR

##### Correlation-based Dissimilarity

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

##### 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/.

##### See Also

##### 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*