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
Accepted author manuscript, 1.5 MB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -