Complex active systems have been proposed as a formalism for modeling real dynamic systems that are organized in a hierarchy of behavioral abstractions. As such, they constitute a conceptual evolution of active systems, a class of discrete-event systems introduced into the literature two decades ago. A complex active system is a hierarchy of active systems, each one characterized by its own behavior expressed by the interaction of several communicating automata. The interaction between active systems within the hierarchy is based on special events, which are generated when specific behavioral patterns occur. Recently, the task of diagnosis of complex active systems has been studied, with an efficient diagnosis technique being proposed. However, the observation of the system is assumed to be linear and certain, which turns out to be an over-assumption in real, large, and distributed systems. This paper extends diagnosis of complex active systems to cope with uncertain temporal observations. An uncertain temporal observation is a DAG where nodes are marked by candidate labels (logical uncertainty), whereas arcs denote partial temporal ordering between nodes (temporal uncertainty). By means of indexing techniques, despite the uncertainty of temporal observations, the intrinsic efficiency of the diagnosis task is retained in both time and space.