diss.ACF: Autocorrelation-based Dissimilarity
In TSclust: Time Series Clustering Utilities

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	diss.ACF(x, y, p = NULL, omega=NULL, lag.max=50) diss.PACF(x, y, p = NULL, omega=NULL, lag.max=50)

`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 `0` to `1`.
`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. `p` is ignored if `omega` is used.

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/.

diss.COR

## 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)