PredDistance: Dissimilarity Measure Based on Nonparametric Forecasts

In TSdist: Distance Measures for Time Series Data

Dissimilarity Measure Based on Nonparametric Forecasts

Description

The dissimilarity of two numerical series of the same length is calculated based on the L1 distance between the kernel estimators of their forecast densities at a given time horizon.

Usage

PredDistance(x, y, h, ...)

Arguments

x	Numeric vector containing the first time series.
y	Numeric vector containing the second time series.
h	Integer value representing the prediction horizon.
...	Additional parameters for the function. See diss.PRED for more information.

Details

This is simply a wrapper for the diss.PRED function of package TSclust. As such, all the functionalities of the diss.PRED 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.series1 and example.series2 are two 
# numeric series of length 100. 

data(example.series1)
data(example.series2)

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

help(example.series)

# Calculate the prediction based distance between the two series using
# the default parameters. 

PredDistance(example.series1, example.series2)

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