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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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| 出版年: | PLoS One |
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| 主要な著者: | , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Public Library of Science
2020
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| 主題: | |
| オンライン･アクセス: | 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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