TSclust (version 1.2.4)

diss.AR.PIC: Model-based Dissimilarity Measure Proposed by Piccolo (1990)

Description

Computes the distance between two time series as the Euclidean distance between the truncated AR operators approximating their ARMA structures.

Usage

diss.AR.PIC(x, y, order.x=NULL, order.y=NULL, permissive=TRUE)

Arguments

x

Numeric vector containing the first of the two time series.

y

Numeric vector containing the second of the two time series.

order.x

Specifies the ARIMA model to be fitted for the series x. When using diss wrapper, use order argument instead. See details.

order.y

Specifies the ARIMA model to be fitted for the series y. When using diss wrapper, use order argument instead. See details.

permissive

Specifies whether to force an AR order of 1 if no order is found. Ignored if neither order.x or order.y are NULL

Value

The computed distance.

Details

If order.x or order.y are NULL, their respective series will be fitted automatically using a AR model. If permissive is TRUE and no AR order is found automatically, an AR order of 1 will be imposed, if this case fails, then no order can be found and the function produces an error. order.x and order.y contain the three components of the ARIMA model: the AR order, the degree of differencing and the MA order, specified as in the function arima.

If using diss function with "AR.PIC" method, the argument order must be used instead of order.x and order.y. orders is a matrix with one row per ARIMA, specified as in arima. If order is NULL, automatic fitting imposing a AR model is performed.

References

Piccolo, D. (1990) A distance measure for classifying arima models. J. Time Series Anal., 11(2), 153--164.

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.AR.MAH, diss.AR.LPC.CEPS, diss, arima, ar

Examples

# NOT RUN {
## Create three sample time series
x <- arima.sim(model=list(ar=c(0.4,-0.1)), n =100, n.start=100)
y <- arima.sim(model=list(ar=c(0.9)), n =100, n.start=100)
z <- arima.sim(model=list(ar=c(0.5, 0.2)), n =100, n.start=100)
## Compute the distance and check for coherent results
#ARIMA(2,0,0) for x and ARIMA(1,0,0) for y
diss.AR.PIC( x, y, order.x = c(2,0,0), order.y = c(1,0,0) )
diss.AR.PIC( x, z, order.x = c(2,0,0), order.y = c(2,0,0) )
# AR for y (automatically selected) and ARIMA(2,0,0) for z
diss.AR.PIC( y, z, order.x=NULL, order.y=c(2,0,0) ) 
#create a dist object for its use with clustering functions like pam or hclust
diss( rbind(x,y,z), METHOD="AR.PIC", order=rbind(c(2,0,0), c(1,0,0), c(2,0,0)) )

# }

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