A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series
数据驱动的两阶段多划分时间序列因果集成模型
Causal inference is a fundamental research topic for discovering the cause-effect relationships in many disciplines. However, not all algorithms are equally well-suited for a given dataset. For instance, some approaches may only be able to identify linear relationships, while others are applicable for non-linearities. Algorithms further vary in their sensitivity to noise and their ability to infer causal information from coupled vs. non-coupled time series. Therefore,…