We present the first whiteness hypothesis test for graphs, i.e., a whiteness test for multivariate time series associated with the nodes of a dynamic graph; as such, the test represents an important model assessment tool for graph deep learning, e.g., in forecasting setups. The statistical test aims at detecting existing serial dependencies among close-in-time observations, as well as spatial dependencies among neighboring observations given the underlying graph. The proposed AZ-test can be intended as a spatio-temporal extension of traditional tests designed for system identification to graph signals. The AZ-test is versatile, allowing the underlying graph to be dynamic, changing in topology and set of nodes over time, and weighted, thus accounting for connections of different strength, as it is the case in many application scenarios like sensor and transportation networks. The asymptotic distribution of the designed test can be derived under the null hypothesis without assuming identically distributed data. We show the effectiveness of the test on both synthetic and real-world problems, and illustrate how it can be employed to assess the quality of spatio-temporal forecasting models by analyzing the prediction residuals appended to the graph stream.