穿越时间的可能性：通过遗传算法为多变量时间序列预测提供合理的反事实解释
Navigating Time's Possibilities: Plausible Counterfactual Explanations for Multivariate Time-Series Forecast through Genetic Algorithms
AI总结：
本文提出一种结合遗传算法和因果性测试的方法，用于多变量时间序列预测中的反事实解释，旨在揭示隐藏的因果关系并预测假设干预下的结果。
摘要：
反事实学习已成为理解和建模复杂动态系统因果关系的有前景方法。本文提出了一种新的反事实学习方法，用于多变量时间序列分析和预测的背景下。主要目标是揭示隐藏的因果关系并识别潜在的干预以实现期望的结果。所提出的方法整合了遗传算法和严谨的因果性测试，以推断和验证时间序列中的反事实依赖性。更具体地说，我们使用格兰杰因果关系来增强识别的因果关系的可靠性，并严格评估其统计显著性。然后，遗传算法与分位数回归相结合，用于利用这些复杂的因果关系来预测未来场景。遗传算法与因果性测试之间的协同作用确保了对数据中时间动态的彻底探索，揭示了隐藏的依赖关系，并使在假设干预下的结果预测成为可能。我们在真实世界数据上评估了我们算法的性能，展示了其处理复杂因果关系的能力，揭示了有意义的反事实见解，并允许在假设干预下的结果预测。
英文摘要：
Counterfactual learning has become promising for understanding and modeling causality in complex and dynamic systems. This paper presents a novel method for counterfactual learning in the context of multivariate time series analysis and forecast. The primary objective is to uncover hidden causal relationships and identify potential interventions to achieve desired outcomes. The proposed methodology integrates genetic algorithms and rigorous causality tests to infer and validate counterfactual dependencies within temporal sequences. More specifically, we employ Granger causality to enhance the reliability of identified causal relationships, rigorously assessing their statistical significance. Then, genetic algorithms, in conjunction with quantile regression, are used to exploit these intricate causal relationships to project future scenarios. The synergy between genetic algorithms and causality tests ensures a thorough exploration of the temporal dynamics present in the data, revealing hidden dependencies and enabling the projection of outcomes under hypothetical interventions. We evaluate the performance of our algorithm on real-world data, showcasing its ability to handle complex causal relationships, revealing meaningful counterfactual insights, and allowing for the prediction of outcomes under hypothetical interventions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
