Online Attention Enhanced Differential and Decomposed LSTM for Time Series Prediction

Due to the time variability and bursty of data, accurate and lag-free time series prediction is difficult and challenging. To address these problems, we propose an online attention enhanced differential and decomposed LSTM (Long Short Term Memory) model called OADDL, which can better capture the com...

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Veröffentlicht in:	IEEE access 2024-01, Vol.12, p.1-1
Hauptverfasser:	Li, Lina, Huang, Shengkui, Liu, Guoxing, Luo, Cheng, Yu, Qinghe, Li, Nianfeng
Format:	Artikel
Sprache:	eng
Schlagworte:	Data models Decomposition Deep learning Difference and decomposition Long short term memory LSTM Online prediction Online services Predictive models Self-attention mechanism Time series Time series analysis Training
Online-Zugang:	Volltext
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