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 |

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, José 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`

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## 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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