K-NN predictions of a histogram time series

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Description

(Beta verson of) Extends the K-NN algorithm for predicting a time series to a histogram time series, using L2 Wasserstein distance.

Usage

1
HTS.predict.knn(HTS, position = length(HTS@data), k = 3)

Arguments

HTS

A HTS object (a histogram time series).

position

an integer, the data histogram to predict

k

the number of neighbours (default=3)

Details

Histogram time series (HTS) describe situations where a distribution of values is available for each instant of time. These situations usually arise when contemporaneous or temporal aggregation is required. In these cases, histograms provide a summary of the data that is more informative than those provided by other aggregates such as the mean. Some fields where HTS are useful include economy, official statistics and environmental science. The function adapts the k-Nearest Neighbours (k-NN) algorithm to forecast HTS and, more generally, to deal with histogram data. The proposed k-NN relies on the L2 Wasserstein distance that is used to measure dissimilarities between sequences of histograms and to compute the forecasts.

Value

a distributionH object predicted from data.

References

Javier Arroyo, Carlos Mate, Forecasting histogram time series with k-nearest neighbours methods, International Journal of Forecasting, Volume 25, Issue 1, January-March 2009, Pages 192-207, ISSN 0169-2070, http://dx.doi.org/10.1016/j.ijforecast.2008.07.003.

Examples

1
prediction=HTS.predict.knn(HTS=RetHTS, position=108, k=3)

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