# diss.PER: Periodogram Based Dissimilarity In TSclust: Time Series Clustering Utilities

## Description

Computes the distance between two time series based on their periodograms.

## Usage

 `1` ```diss.PER(x, y, logarithm=FALSE, normalize=FALSE) ```

## Arguments

 `x` Numeric vector containing the first of the two time series. `y` Numeric vector containing the second of the two time series. `logarithm` Boolean. If `TRUE` logarithm of the periodogram coefficients will be taken. `normalize` Boolean. If `TRUE`, the periodograms will be normalized by the variance of their respective series.

## Details

Computes the Euclidean distance between the periodogram coefficients of the series `x` and `y`. Additional transformations can be performed on the coefficients depending on the values of `logarithm` and `normalize`.

## Value

The computed distance.

## Author(s)

Pablo Montero Manso, José Antonio Vilar.

## References

Caiado, J., Crato, N. and Peña, D. (2006) A periodogram-based metric for time series classification. Comput. Statist. Data Anal., 50(10), 2668–2684.

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/.

`link{diss.INT.PER}`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## 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.PER(x, y) diss.PER(x, z) diss.PER(y, z) diss.PER(x, y, TRUE, TRUE) diss.PER(x, z, TRUE, TRUE) diss.PER(y, z, TRUE, TRUE) #create a dist object for its use with clustering functions like pam or hclust diss( rbind(x,y,z), "PER", logarithm=TRUE, normalize=TRUE) ```