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Updated: May 9, 2025

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
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Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

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学习时间特征与交替相似性和近距离注意力时间序列预测

Jingyang Chen, Ping Li, Jiancheng Lv

    IEEE transactions on neural networks and learning systems
    |April 30, 2025
    PubMed
    概括

    我们介绍了交替相似性和近距离注意力 (ASAP注意力),这是一种用于长期时间序列预测的新方法. 通过更好地捕捉复杂的数据依赖性,以获得更准确的预测,ASAP注意力改进了标准的注意力机制.

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