FastPoint: Scalable Deep Point Processes [chapter]

Ali Caner Türkmen, Yuyang Wang, Alexander J. Smola
2020 Lecture Notes in Computer Science  
We propose FastPoint, a novel multivariate point process that enables fast and accurate learning and inference. FastPoint uses deep recurrent neural networks to capture complex temporal dependency patterns among different marks, while self-excitation dynamics within each mark are modeled with Hawkes processes. This results in substantially more efficient learning and scales to millions of correlated marks with superior predictive accuracy. Our construction also allows for efficient and parallel
more » ... sequential Monte Carlo sampling for fast predictive inference. FastPoint outperforms baseline methods in prediction tasks on synthetic and real-world high-dimensional event data at a small fraction of the computational cost.
doi:10.1007/978-3-030-46147-8_28 fatcat:el3yecoypbcydbhmvqeshmnpk4