Causality defines the relationship between cause and effect. In multivariate time series field, this notion allows to characterize the links between several time series considering temporal lags. These phenomena are particularly important in medicine to analyze the effect of a drug for example, in manufacturing to detect the causes of an anomaly in a complex system or in social sciences... Most of the time, studying these complex systems is made through correlation only. But correlation can lead to spurious relationships. To circumvent this problem, we present in this paper a novel approach for discovering causality in time series data that combines a causal discovery algorithm with an information theoretic-based measure. Hence the proposed method allows inferring both linear and non-linear relationships and building the underlying causal graph. We evaluate the performance of our approach on several simulated data sets, showing promising results.

翻译：因果关系定义了原因与结果之间的联系。在多变量时间序列领域，这一概念允许考虑时间滞后来描述多个时间序列之间的关联。这些现象在医学中分析药物效果、在制造业中检测复杂系统异常的原因，以及在社会科学中尤为重要。多数情况下，研究这些复杂系统仅通过相关性分析进行。但相关性可能导致虚假关系。为解决此问题，本文提出了一种新颖的时间序列因果发现方法，该方法将因果发现算法与基于信息论的度量相结合。所提出的方法能够推断线性和非线性关系，并构建底层因果图。我们在多个模拟数据集上评估了该方法的性能，结果显示其具有良好效果。