Deep Fourier Kernel for Self-Attentive Point Processes
This addresses the problem of capturing non-linear temporal patterns in event data for applications like healthcare or finance, representing an incremental improvement over existing attention-based methods.
The authors tackled modeling complex temporal dependencies in discrete event data by developing an attention-based point process model with a novel Fourier kernel score function, achieving competitive performance with state-of-the-art methods on synthetic and real data.
We present a novel attention-based model for discrete event data to capture complex non-linear temporal dependence structures. We borrow the idea from the attention mechanism and incorporate it into the point processes' conditional intensity function. We further introduce a novel score function using Fourier kernel embedding, whose spectrum is represented using neural networks, which drastically differs from the traditional dot-product kernel and can capture a more complex similarity structure. We establish our approach's theoretical properties and demonstrate our approach's competitive performance compared to the state-of-the-art for synthetic and real data.