  Multivariate time series forecasting poses challenges as the variables are
intertwined in time and space, like in the case of traffic signals. Defining
signals on graphs relaxes such complexities by representing the evolution of
signals over a space using relevant graph kernels such as the heat diffusion
kernel. However, this kernel alone does not fully capture the actual dynamics
of the data as it only relies on the graph structure. The gap can be filled by
combining the graph kernel representation with data-driven models that utilize
historical data. This paper proposes a traffic propagation model that merges
multiple heat diffusion kernels into a data-driven prediction model to forecast
traffic signals. We optimize the model parameters using Bayesian inference to
minimize the prediction errors and, consequently, determine the mixing ratio of
the two approaches. Such mixing ratio strongly depends on training data size
and data anomalies, which typically correspond to the peak hours for traffic
data. The proposed model demonstrates prediction accuracy comparable to that of
the state-of-the-art deep neural networks with lower computational effort. It
notably achieves excellent performance for long-term prediction through the
inheritance of periodicity modeling in data-driven models.
