ARLPCCepsDistance {TSdist}

Dissimilarity Based on LPC Cepstral Coefficients

Description

Computes the dissimilarity between two numeric time series in terms of their Linear Predictive Coding (LPC) ARIMA processes.

Usage

ARLPCCepsDistance(x, y, ...)

Arguments

x

Numeric vector containing the first time series.

y

Numeric vector containing the second time series.

...

Additional parameters for the function. See diss.AR.LPC.CEPS for more information.

Details

This is simply a wrapper for the diss.AR.LPC.CEPS function of package TSclust. As such, all the functionalities of the diss.AR.LPC.CEPS function are also available when using this function.

Value

d

The computed distance between the pair of series.

Author(s)

Usue Mori, Alexander Mendiburu, Jose A. Lozano.

References

Pablo Montero, José A. Vilar (2014). TSclust: An R Package for Time Series Clustering. Journal of Statistical Software, 62(1), 1-43. URL http://www.jstatsoft.org/v62/i01/.

See Also

To calculate this distance measure using ts, zoo or xts objects see TSDistances. To calculate distance matrices of time series databases using this measure see TSDatabaseDistances.

Examples

# The objects example.series3 and example.series4 are two 
# numeric series of length 100 and 120 contained in the 
# TSdist package obtained from an ARIMA(3,0,2) process. 

data(example.series3)
data(example.series4)

# For information on their generation and shape see 
# help page of example.series.

help(example.series)

# Calculate the ar.lpc.ceps distance between the two series using
# the default parameters. In this case an AR model is automatically 
# selected for each of the series:

ARLPCCepsDistance(example.series3, example.series4)

# Calculate the ar.lpc.ceps distance between the two series
# imposing the order of the ARIMA model of each series:

ARLPCCepsDistance(example.series3, example.series4, order.x=c(3,0,2), 
order.y=c(3,0,2))