Point Process Flows
This work addresses the challenge of accurately modeling asynchronous and probabilistic event sequences for applications in fields like finance or healthcare, representing an incremental improvement by introducing a new method for a known bottleneck.
The authors tackled the problem of modeling event sequences by proposing an intensity-free framework using normalizing flows to directly model point process distributions, which effectively captures complex temporal patterns without restrictive parametric forms, as shown in comparisons with state-of-the-art models on synthetic and real-life datasets.
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature. We propose an intensity-free framework that directly models the point process distribution by utilizing normalizing flows. This approach is capable of capturing highly complex temporal distributions and does not rely on restrictive parametric forms. Comparisons with state-of-the-art baseline models on both synthetic and challenging real-life datasets show that the proposed framework is effective at modeling the stochasticity of discrete event sequences.