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

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/diss.R

Computes the dissimilarity between two time series in terms of the distance between their integrated periodograms.

1	diss.INT.PER(x, y, normalize=TRUE)

`x`	Numeric vector containing the first of the two time series.
`y`	Numeric vector containing the second of the two time series.
`normalize`	If `TRUE` the normalized version is computed.

The distance is computed as:

d(x,y) = INT( F_x(λ) - F_y(λ))dλ | λ = -π to π

where F_x(λ_j)=(sum F_x(λ_i)|i=1 to j)/C_x and F_y(λ_j)=(sum F_y(λ_i)|i=1 to j)/C_xy, with C_x = ∑_i I_x(λ_i) and C_y = ∑_i I_y(λ_i) in the normalized version. C_x = 1 and C_y = 1 in the non-normalized version. I_x(λ_k) and I_y(λ_k) denote the periodograms of x and y, respectively.

The computed distance.

Pablo Montero Manso, Jos<c3><a9> Antonio Vilar.

Casado de Lucas, D. (2010) Classification techniques for time series and functional data.

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.PER

## 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.INT.PER(x, y, normalize=TRUE)
diss.INT.PER(x, y, normalize=TRUE)
diss.INT.PER(x, y, normalize=TRUE)
## Not run: 
diss( rbind(x,y,z), "INT.PER", normalize=FALSE )

## End(Not run)