ACFDistance: Autocorrelation-based Dissimilarity

In TSdist: Distance Measures for Time Series Data

Autocorrelation-based Dissimilarity

Description

Computes the dissimilarity between a pair of numeric time series based on their estimated autocorrelation coefficients.

Usage

ACFDistance(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.ACF for more information.

Details

This is simply a wrapper for the diss.ACF function of package TSclust. As such, all the functionalities of the diss.ACF 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/.

Galeano, P., & Pella, D. (2000). Multivariate Analysis in Vector Time Series Pedro Galeano and Daniel Pella. Resenhas, the Journal of the Institute of Mathematics and Statistics of the University of Sao Paolo, 4, 383–403.

Lei, H., & Sun, B. (2007). A Study on the Dynamic Time Warping in Kernel Machines. In 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (pp. 839–845).

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. 

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

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

help(example.series)

# Calculate the autocorrelation based distance between the two series using
# the default parameters:

ACFDistance(example.series3, example.series4)

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