# diss.COR: Correlation-based Dissimilarity In TSclust: Time Series Clustering Utilities

## Description

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

## Usage

 `1` ```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 = √{ 2 ( 1 - ρ) } is computed, where (ρ) denotes the Pearson's correlation between series `x` and `y`. If `beta` is specified, the function d_2 = √( ((1 - ρ) / (1 + ρ))^β ) is used, where β is `beta` .

## Value

The computed distance.

## Author(s)

Pablo Montero Manso, José Antonio Vilar.

## 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`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```## 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") ## End(Not run) ```

### Example output

```Loading required package: wmtsa
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE