时间序列因果关系的发现通过特征重要性和整体模型的发现
Manuel Castro1, Pedro Ribeiro Mendes Júnior2, Aurea Soriano-Vargas2
1Artificial Intelligence Lab., Recod.ai, Institute of Computing, University of Campinas (Unicamp), 13083-852, Campinas, SP, Brazil. castroavila@ic.unicamp.br.
Scientific reports
|July 14, 2023
概括
本研究使用集体机器学习模型从观测数据中推断因果关系. 该方法成功地确定了油田生产中的因果关系,并通过现有数据验证了发现.
科学领域:
- 机器学习 机器学习
- 因果推理因果推理
- 时间序列分析时间序列分析
背景情况:
- 从观测数据中解释复杂的机器学习模型具有挑战性.
- 越来越复杂的数据阻碍了对模型决策过程的理解.
- 因果推理对于可靠的预测和模型可解释性至关重要.
研究的目的:
- 提出一种新的方法来从观察时间序列数据中推断因果关系.
- 在复杂的数据集中利用集合模型进行因果发现.
- 确定拟议方法在确定油田生产中井间连接方面的有效性.
主要方法:
- 利用集体模型,特别是随机森林,用于特征重要性分析.
- 开发了一种代预测方法来识别因果驱动因素.
- 使用合成数据集和现实世界油田生产数据与追踪信息验证了方法.
主要成果:
- 拟议的方法成功地在合成和真实油田数据集中确定了因果关系.
- 因果分析结果与来自油田的追踪器数据的确认间隔连接一致.
- 通过评估预测模型中的特征重要性,证明了建立因果网络的能力.
结论:
- 该方法提供了一种可靠的方法,用于使用观测时间序列数据进行因果发现.
- 集合模型为揭示复杂系统中隐藏的因果关系提供了一个强大的工具.
- 这项研究开创了利用生产数据对油田井间连接的因果分析的开创性.
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