Modeling of Spatio-Temporal Hawkes Processes with Randomized Kernels
This work addresses spatio-temporal event analysis for applications like crime prediction and traffic forecasting, representing an incremental improvement in inference methods for Hawkes processes.
The paper tackles the problem of inferring spatio-temporal event dynamics using Hawkes processes, introducing a novel inference framework with randomized Fourier features and gradient descent, which improves fitting capability on synthetic and real datasets compared to conventional methods.
We investigate spatio-temporal event analysis using point processes. Inferring the dynamics of event sequences spatiotemporally has many practical applications including crime prediction, social media analysis, and traffic forecasting. In particular, we focus on spatio-temporal Hawkes processes that are commonly used due to their capability to capture excitations between event occurrences. We introduce a novel inference framework based on randomized transformations and gradient descent to learn the process. We replace the spatial kernel calculations by randomized Fourier feature-based transformations. The introduced randomization by this representation provides flexibility while modeling the spatial excitation between events. Moreover, the system described by the process is expressed within closed-form in terms of scalable matrix operations. During the optimization, we use maximum likelihood estimation approach and gradient descent while properly handling positivity and orthonormality constraints. The experiment results show the improvements achieved by the introduced method in terms of fitting capability in synthetic and real datasets with respect to the conventional inference methods in the spatio-temporal Hawkes process literature. We also analyze the triggering interactions between event types and how their dynamics change in space and time through the interpretation of learned parameters.