  Neural Temporal Point Processes (TPPs) have emerged as the primary framework
for predicting sequences of events that occur at irregular time intervals, but
their sequential nature can hamper performance for long-horizon forecasts. To
address this, we introduce a novel approach that incorporates a diffusion
generative model. The model facilitates sequence-to-sequence prediction,
allowing multi-step predictions based on historical event sequences. In
contrast to previous approaches, our model directly learns the joint
probability distribution of types and inter-arrival times for multiple events.
This allows us to fully leverage the high dimensional modeling capability of
modern generative models. Our model is composed of two diffusion processes, one
for the time intervals and one for the event types. These processes interact
through their respective denoising functions, which can take as input
intermediate representations from both processes, allowing the model to learn
complex interactions. We demonstrate that our proposal outperforms
state-of-the-art baselines for long-horizon forecasting of TPP.
