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

Computes the dissimilarity between two time series as the distance between their estimated simple (ACF) or partial (PACF) autocorrelation coefficients.

1 2 |

`x` |
Numeric vector containing the first of the two time series. |

`y` |
Numeric vector containing the second of the two time series. |

`p` |
If not NULL, sets the weight for the geometric decaying of the autocorrelation coefficients. Ranging from |

`lag.max` |
Maximum number of simple or partial autocorrelation coefficients to be considered. |

`omega` |
If not NULL, completely specifies the weighting matrix for the autocorrelation coefficients. |

Performs the weighted Euclidean distance between the simple autocorrelation ( `dist.ACF`

) or partial autocorrelation ( `dist.PACF`

) coefficients.
If neither `p`

nor `omega`

are specified, uniform weighting is used. If `p`

is specified, geometric wights decaying with the lag in the form * p(1-p)^i* are applied. If `omega`

(*Ω*) is specified,

* d(x,y) = ((ρ_x - ρ_y)^t Ω (ρ_x - ρ_y) )^0.5*

with *ρ_x* and *ρ_y* the respective (partial) autocorrelation coefficient vectors.

The computed distance.

Pablo Montero Manso, José Antonio Vilar.

Galeano, P. and Peña, D. (2000). Multivariate analysis in vector time series. *Resenhas*, **4 (4)**, 383–403.

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/.

1 2 3 4 5 6 7 8 9 10 | ```
## 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.PACF(x, y)
diss.ACF(x, z)
diss.PACF(y, z)
#create a dist object for its use with clustering functions like pam or hclust
diss( rbind(x,y,z), "ACF", p=0.05)
``` |

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