Learning K-U-Net with constant complexity: An Application to time series forecasting

Training deep models for time series forecasting is a critical task with an inherent challenge of time complexity. While current methods generally ensure linear time complexity, our observations on temporal redundancy show that high-level features are learned 98.44\% slower than low-level features....

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Hauptverfasser:	You, Jiang, Cela, Arben, Natowicz, René, Ouanounou, Jacob, Siarry, Patrick
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning
Online-Zugang:	Volltext bestellen
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