Data-driven approaches are becoming increasingly crucial for modeling and performance

monitoring of complex dynamical systems. Such necessity stems from complex interactions

among sub-systems and high dimensionality that render majority of rst-principle based

methods insucient. This work explores the capability of a recently proposed probabilistic

graphical modeling technique called spatiotemporal pattern network (STPN) in capturing

Granger causality among observations in a dynamical system. In this context, we introduce

the notion of Granger-STPN (G-STPN) inspired by the notion of Granger causality. We

compare the metrics used in the two frameworks for increasing memory in a dynamical

system, and show that the metric for G-STPN can be approximated by transfer entropy.

We apply this new framework for anomaly detection and root cause analysis in a robotic

platform.