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Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series
Hierarchical time series is a set of data sequences organized by aggregation constraints to represent many real-world applications in research and the industry. Forecasting of hierarchical time series is a challenging and time-consuming problem owing to ensuring the forecasting consistency among the...
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| Publicado en: | Sensors (Basel) |
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| Autores principales: | , , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
MDPI
2021
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8271891/ https://ncbi.nlm.nih.gov/pubmed/34206750 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s21134379 |
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