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Prediction of hierarchical time series using structured regularization and its application to artificial neural networks

This paper discusses the prediction of hierarchical time series, where each upper-level time series is calculated by summing appropriate lower-level time series. Forecasts for such hierarchical time series should be coherent, meaning that the forecast for an upper-level time series equals the sum of...

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Bibliografiska uppgifter
I publikationen:PLoS One
Huvudupphovsmän: Shiratori, Tomokaze, Kobayashi, Ken, Takano, Yuichi
Materialtyp: Artigo
Språk:Inglês
Publicerad: Public Library of Science 2020
Ämnen:
Länkar:https://ncbi.nlm.nih.gov/pmc/articles/PMC7660543/
https://ncbi.nlm.nih.gov/pubmed/33180811
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0242099
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