Cross-validation aggregation for combining autoregressive neural network forecasts

Volume: 32, Issue: 4, Pages: 1120 - 1137
Published: Oct 1, 2016
Abstract
This paper evaluates k-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...
Paper Details
Title
Cross-validation aggregation for combining autoregressive neural network forecasts
Published Date
Oct 1, 2016
Volume
32
Issue
4
Pages
1120 - 1137
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