Title: Learning to Decouple Complex System for Sequential Data. Abstract: A complex system with cluttered observations may be a coupled mixture of multiple simple sub-systems corresponding to \emph{latent entities}. Such sub-systems may hold distinct dynamics in the continuous-time domain, therein complicated interactions between sub-systems also evolve over time.  This setting is fairly common in the real world, but has been less considered. In this paper, we propose a sequential learning approach under this setting by decoupling a complex system for handling irregularly sampled and cluttered sequential observations. Such decoupling brings about not only subsystems describing the dynamics of each latent entity, but also a meta-system capturing the interaction between entities over time. Specifically, we argue that the meta-system of interactions is governed by a smoothed version of \emph{projected differential equations}. Experimental results on synthetic and real-world datasets show the advantages of our approach when facing complex and cluttered sequential data compared to the state-of-the-art.