# Correlation-based Dissimilarity

### Description

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

### Usage

1 |

### 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/.

### See Also

`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)
``` |