Cross-validation aggregation for combining autoregressive neural network forecasts

Barrow, Devon Kennard and Crone, Sven Friedrich Werner Manfred (2016) Cross-validation aggregation for combining autoregressive neural network forecasts. International Journal of Forecasting, 32 (4). pp. 1120-1137. ISSN 0169-2070

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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.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Forecasting
Additional Information:
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
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1400/1403
Subjects:
ID Code:
80093
Deposited By:
Deposited On:
20 Jun 2017 14:24
Refereed?:
Yes
Published?:
Published
Last Modified:
27 Oct 2020 05:53