Coherent probabilistic forecasts for hierarchical time series

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Abstract

Many applications require forecasts for a hierarchy comprising a set of time scries along with aggregates of subsets of these scries. Hierarchical forecasting require not only good prediction accuracy at each level of the hierarchy, but also the coherency between different levels-the property that forecasts add up appropriately across the hierarchy. A fundamental limitation of prior research is the focus on forecasting the mean of each time series. We consider the situation where probabilistic forecasts are needed for each series in the hierarchy, and propose an algorithm to compute predictive distributions rather than mean forecasts only. Our algorithm has the advantage of synthesizing information from different levels in the hierarchy through a sparse forecast combination and a probabilistic hierarchical aggregation. We evaluate the accuracy of our forecasting algorithm on both simulated data and large-scale electricity smart meter data. The results show consistent performance gains compared to state-of-the art methods.

Original languageEnglish
Title of host publicationProceedings of the 34th International Conference on Machine Learning
EditorsDoina Precup, Yee Whye Teh
Place of PublicationMassachusetts USA
PublisherProceedings of Machine Learning Research (PMLR)
Pages3348-3357
Number of pages10
Publication statusPublished - 1 Jan 2017
EventInternational Conference on Machine Learning 2017 - International Convention Centre , Sydney , Australia
Duration: 6 Aug 201711 Aug 2018
Conference number: 34th
https://icml.cc/Conferences/2017
https://2017.icml.cc/

Publication series

NameProceedings of Machine Learning Research
Volume70
ISSN (Print)1938-7228

Conference

ConferenceInternational Conference on Machine Learning 2017
Abbreviated titleICML 2017
CountryAustralia
CitySydney
Period6/08/1711/08/18
Internet address

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