Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe
This work provides a faster method for modeling sequential events in domains like finance and epidemiology, but it is incremental as it builds on existing optimization techniques.
The authors tackled the problem of learning multidimensional Hawkes processes by adapting the Frank-Wolfe algorithm, achieving better or comparable accuracy in parameter estimation and significantly faster runtime compared to other first-order methods.
Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data. Multidimensional Hawkes processes model both the self and cross-excitation between different types of events and have been applied successfully in various domain such as finance, epidemiology and personalized recommendations, among others. In this work we present an adaptation of the Frank-Wolfe algorithm for learning multidimensional Hawkes processes. Experimental results show that our approach has better or on par accuracy in terms of parameter estimation than other first order methods, while enjoying a significantly faster runtime.