Developing a multivariate time series forecasting framework based on stacked autoencoders and multi-phase feature
Time series forecasting across different domains has received massive attention as it eases intelligent decision-making activities. Recurrent neural networks and various deep learning algorithms have been applied to modeling and forecasting multivariate time series data. Due to intricate non-linear...
Asıl Yazarlar: | , , , , |
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Materyal Türü: | Makale |
Dil: | English |
Baskı/Yayın Bilgisi: |
Elsevier
2024-04-01
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Seri Bilgileri: | Heliyon |
Konular: | |
Online Erişim: | http://www.sciencedirect.com/science/article/pii/S240584402403891X |