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    Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 32, 4, 2016 DOI: 10.1016/j.ijforecast.2015.12.011

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Cross-validation aggregation for combining autoregressive neural network forecasts

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Cross-validation aggregation for combining autoregressive neural network forecasts. / Barrow, Devon Kennard; Crone, Sven Friedrich Werner Manfred.

In: International Journal of Forecasting, Vol. 32, No. 4, 01.10.2016, p. 1120-1137.

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@article{7da19eb513da41f89d9e369e27c42ae0,
title = "Cross-validation aggregation for combining autoregressive neural network forecasts",
abstract = "This paper evaluates kk-fold and Monte Carlo cross-validation and aggregation (crogging) for combining neural network autoregressive forecasts. We introduce Monte Carlo crogging which combines bootstrapping and cross-validation (CV) in a single approach through repeated random splitting of the original time series into mutually exclusive datasets for training. As the training/validation split is independent of the number of folds, the algorithm offers more flexibility in the size, and number of training samples compared to kk-fold cross-validation. The study also provides for crogging and bagging: (1) the first systematic evaluation across time series length and combination size, (2) a bias and variance decomposition of the forecast errors to understand improvement gains, and (3) a comparison to established benchmarks of model averaging and selection. Crogging can easily be extended to other autoregressive models. Results on real and simulated series demonstrate significant improvements in forecasting accuracy especially for short time series and long forecast horizons.",
keywords = "Forecast combination, Bootstrapping, Monte Carlo, Time series, Cross-validation",
author = "Barrow, {Devon Kennard} and Crone, {Sven Friedrich Werner Manfred}",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 32, 4, 2016 DOI: 10.1016/j.ijforecast.2015.12.011",
year = "2016",
month = oct,
day = "1",
doi = "10.1016/j.ijforecast.2015.12.011",
language = "English",
volume = "32",
pages = "1120--1137",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier Science B.V.",
number = "4",

}

RIS

TY - JOUR

T1 - Cross-validation aggregation for combining autoregressive neural network forecasts

AU - Barrow, Devon Kennard

AU - Crone, Sven Friedrich Werner Manfred

N1 - This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 32, 4, 2016 DOI: 10.1016/j.ijforecast.2015.12.011

PY - 2016/10/1

Y1 - 2016/10/1

N2 - This paper evaluates kk-fold and Monte Carlo cross-validation and aggregation (crogging) for combining neural network autoregressive forecasts. We introduce Monte Carlo crogging which combines bootstrapping and cross-validation (CV) in a single approach through repeated random splitting of the original time series into mutually exclusive datasets for training. As the training/validation split is independent of the number of folds, the algorithm offers more flexibility in the size, and number of training samples compared to kk-fold cross-validation. The study also provides for crogging and bagging: (1) the first systematic evaluation across time series length and combination size, (2) a bias and variance decomposition of the forecast errors to understand improvement gains, and (3) a comparison to established benchmarks of model averaging and selection. Crogging can easily be extended to other autoregressive models. Results on real and simulated series demonstrate significant improvements in forecasting accuracy especially for short time series and long forecast horizons.

AB - This paper evaluates kk-fold and Monte Carlo cross-validation and aggregation (crogging) for combining neural network autoregressive forecasts. We introduce Monte Carlo crogging which combines bootstrapping and cross-validation (CV) in a single approach through repeated random splitting of the original time series into mutually exclusive datasets for training. As the training/validation split is independent of the number of folds, the algorithm offers more flexibility in the size, and number of training samples compared to kk-fold cross-validation. The study also provides for crogging and bagging: (1) the first systematic evaluation across time series length and combination size, (2) a bias and variance decomposition of the forecast errors to understand improvement gains, and (3) a comparison to established benchmarks of model averaging and selection. Crogging can easily be extended to other autoregressive models. Results on real and simulated series demonstrate significant improvements in forecasting accuracy especially for short time series and long forecast horizons.

KW - Forecast combination

KW - Bootstrapping

KW - Monte Carlo

KW - Time series

KW - Cross-validation

U2 - 10.1016/j.ijforecast.2015.12.011

DO - 10.1016/j.ijforecast.2015.12.011

M3 - Journal article

VL - 32

SP - 1120

EP - 1137

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 4

ER -