diss.INT.PER: Integrated Periodogram Based Dissimilarity

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

Description

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

Usage

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

Arguments

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.

Details

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.

Value

The computed distance.

Author(s)

Pablo Montero Manso, José Antonio Vilar.

References

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/.

See Also

diss.PER

Examples

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## 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)


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