Causal discovery for time series with constraint-based model and PMIME measure

We develop a method that addresses the problem of causality for multivariate time series with few assumptions. It consists of Causality defines the relationship between cause and effect. In multivariate merging a causal discovery algorithm, the PC algorithm [6], with an time series field, this notion allows to characterize the information theoretic-based causal inference measure, the Partial links between several time series considering temporal lags. These Mutual Information from Mixed Embedding [7] (PMIME). Based on phenomena are particularly important in medicine to analyze the information theory, the PMIME allows to limit assumptions on the effect of a drug for example, in manufacturing to detect the causes data but also on the links between time series. With this measure, of an anomaly in a complex system or in social sciences... Most of the PC algorithm gives causal relationships among multivariate the time, studying these complex systems is made through correlation time series, by representing the causality with a Directed Acyclic only. But correlation can lead to spurious relationships.