R: Dissimilarity Measure Based on Nonparametric Forecasts

PredDistance {TSdist} | R Documentation

## 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)

[Package TSdist version 3.6 Index]