Context-Aware Diffusion in Temporal Event Streams

In the context of continuous time event sequence prediction, diffusion models have achieved state-of-the-art performance. These methods propose to model the entire event sequence by simultaneously diffusing both the time intervals and the event types of the target sequence. However, they employ the default forward noising process, which does not take into consideration the local structural and temporal patterns observed in event sequences. In this work, we propose a locality-based structure-aware noising technique, which customizes the noise scale at each event, considering temporal patterns in the neighborhood. Building on this framework, we further develop specialized training and sampling procedures tailored to the proposed noising mechanism and provide theoretical analysis to support our design choices. We validate our approach in five real-world datasets while comparing several state-of-the-art baselines for both long- and short-horizon forecasting tasks. The proposed method achieves up to 20.11% in time prediction and up to 7.91% in type prediction over the most competitive baseline. Our quantitative and qualitative evaluations establish the effectiveness of data-driven locality-aware noising in diffusion models for continuous-time event sequences