TSclust (version 1.3.2)

diss.COR: Correlation-based Dissimilarity

Description

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

Usage

diss.COR(x, y, beta = NULL)

Value

The computed distance.

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.

Author

Pablo Montero Manso, José Antonio Vilar.

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 .

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. tools:::Rd_expr_doi("doi:10.18637/jss.v062.i01")

See Also

diss.PACF, diss.ACF, diss

Examples

## 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
# \donttest{
diss( rbind(x,y,z), "COR")
# }