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Updated: Sep 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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使用现实世界的测试案例对时间序列的缺失数据归算方法进行基准测试

Adedolapo Aishat Toye1, Asuman Celik1, Samantha Kleinberg1

  • 1Department of Computer Science, Stevens Institute of Technology, USA.

Proceedings of machine learning research
|September 2, 2025
PubMed
概括

在随机缺失的数据,而不是现实的模式上运行最好. 线性插入显示所有缺失数据类型的误差最低,强调需要更好的复杂缺失的评估和归算技术.

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