# diss.PER

##### Periodogram Based Dissimilarity

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

##### Usage

`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.

##### References

Caiado, J., Crato, N. and Pe<U+00F1>a, 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/.

##### See Also

`link{diss.INT.PER}`

##### 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.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)
# }
```

*Documentation reproduced from package TSclust, version 1.2.4, License: GPL-2*