Exploring the sources of uncertainty: why does bagging for time series forecasting work?
type: Item
instance of: scholarly article
title: Exploring the sources of uncertainty: why does bagging for time series forecasting work?
zbMATH Open document ID: 1403.62169
author: Fotios Petropoulos
author: Rob J. Hyndman
author: Christoph Bergmeir
published in: European Journal of Operational Research
publication date: 2018-05-30T00:00:00Z
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 91B84
zbMATH DE Number: 6876721
zbMATH Keywords: forecasting
zbMATH Keywords: bootstrapping
zbMATH Keywords: aggregation
zbMATH Keywords: uncertainty
zbMATH Keywords: model combination
describes a project that uses: Boot.EXPOS
describes a project that uses: R
describes a project that uses: forecast
describes a project that uses: Forecast
describes a project that uses: ElemStatLearn
describes a project that uses: GitHub
MaRDI profile type: Publication
full work available at URL: j.ejor.2018.01.045
OpenAlex ID: W2787031726
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cites work: `Horses for courses' in demand forecasting
DOI: 10.1016/J.EJOR.2018.01.045
description: scientific article; zbMATH DE number 6876721