  Continuous-time long-term event prediction plays an important role in many
application scenarios. Most existing works rely on autoregressive frameworks to
predict event sequences, which suffer from error accumulation, thus
compromising prediction quality. Inspired by the success of denoising diffusion
probabilistic models, we propose a diffusion-based non-autoregressive temporal
point process model for long-term event prediction in continuous time. Instead
of generating events one at a time in an autoregressive way, our model predicts
the future event sequence entirely as a whole. In order to perform diffusion
processes on event sequences, we develop a bidirectional map between target
event sequences and the Euclidean vector space. Furthermore, we design a novel
denoising network to capture both sequential and contextual features for better
sample quality. Extensive experiments are conducted to prove the superiority of
our proposed model over state-of-the-art methods on long-term event prediction
in continuous time. To the best of our knowledge, this is the first work to
apply diffusion methods to long-term event prediction problems.
