A comparative framework for multi-horizon time series forecasting: Neural networks with adaptive preprocessing
Accurate multi-horizon time series forecasting remains a major challenge in predictive modeling due to cumulative error propagation over extended horizons. This study proposes a unified and reproducible framework that integrates adaptive signal decomposition with neural network architectures under a...
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| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Elsevier
2025-12-01
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| coleção: | Machine Learning with Applications |
| Assuntos: | |
| Acesso em linha: | http://www.sciencedirect.com/science/article/pii/S2666827025001641 |
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