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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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MDPI
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271891/ https://www.ncbi.nlm.nih.gov/pubmed/34206750 http://dx.doi.org/10.3390/s21134379 |