Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Prediction

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.

Intensity-free Convolutional Temporal Point Process: Incorporating Local and Global Event Contexts

Event prediction in the continuous-time domain is a crucial but rather difficult task. Temporal point process (TPP) learning models have shown great advantages in this area. Existing models mainly focus on encoding global contexts of events using techniques like recurrent neural networks (RNNs) or self-attention mechanisms. However, local event contexts also play an important role in the occurrences of events, which has been largely ignored. Popular convolutional neural networks, which are designated for local context capturing, have never been applied to TPP modelling due to their incapability of modelling in continuous time. In this work, we propose a novel TPP modelling approach that combines local and global contexts by integrating a continuous-time convolutional event encoder with an RNN. The presented framework is flexible and scalable to handle large datasets with long sequences and complex latent patterns. The experimental result shows that the proposed model improves the performance of probabilistic sequential modelling and the accuracy of event prediction. To our best knowledge, this is the first work that applies convolutional neural networks to TPP modelling.