RSMformer: an efficient multiscale transformer-based framework for long sequence time-series forecasting

Long sequence time-series forecasting (LSTF) is a significant and challenging task. Many real-world applications require long-term forecasting of time series. In recent years, Transformer-based models have emerged as a promising solution for addressing LSTF tasks. Nevertheless, the model’s performan...

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Veröffentlicht in:	Applied intelligence (Dordrecht, Netherlands) Netherlands), 2024-01, Vol.54 (2), p.1275-1296
Hauptverfasser:	Tong, Guoxiang, Ge, Zhaoyuan, Peng, Dunlu
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Artificial Intelligence Complexity Computer Science Deep learning Forecasting Machines Manufacturing Mathematical models Mechanical Engineering Neural networks Processes Sparsity Time series Wavelet transforms
Online-Zugang:	Volltext
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