ARLPCCepsDistance: Dissimilarity Based on LPC Cepstral Coefficients

In TSdist: Distance Measures for Time Series Data

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))

TSdist documentation built on Aug. 31, 2022, 5:09 p.m.