Model selection in reconciling hierarchical time series

DOI10.1007/s10994-021-06126-zOpenAlexW4205233430MaRDI QIDQ2127263

Rob Hyndman, Evangelos Spiliotis, Mahdi Abolghasemi, Christoph Bergmeir

Publication date: 20 April 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2010.10742

zbMATH Keywords

classificationmachine learninghierarchical forecastingtime series features

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• forecast
• Kernlab
• Forecast
• XGBoost
• hts
• FFORMA
• tsfeatures
• catch22
• fpp3

Cites Work

• Optimal combination forecasts for hierarchical time series
• Testing the null hypothesis of stationarity against the alternative of a unit root. How sure are we that economic time series have a unit root?
• An investigation of aggregate variable time series forecast strategies with specific subaggregate time series statistical correlation
• Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model.
• Fast computation of reconciled forecasts for hierarchical and grouped time series
• Exploring the sources of uncertainty: why does bagging for time series forecasting work?
• Classification by pairwise coupling
• `Horses for courses' in demand forecasting
• Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization
• Forecasting item-level demands: an analytical evaluation of top-down versus bottom-up forecasting in a production-planning framework
• On the effectiveness of top‐down strategy for forecasting autoregressive demands

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