Event prediction aims to forecast the time and type of a future event based
on a historical event sequence. Despite its significance, several challenges
exist, including the irregularity of time intervals between consecutive events,
the existence of cycles, periodicity, and multi-scale event interactions, as
well as the high computational costs for long event sequences. Existing neural
temporal point processes (TPPs) methods do not capture the multi-scale nature
of event interactions, which is common in many real-world applications such as
clinical event data. To address these issues, we propose the
cross-temporal-scale transformer (XTSFormer), designed specifically for
irregularly timed event data. Our model comprises two vital components: a novel
Feature-based Cycle-aware Time Positional Encoding (FCPE) that adeptly captures
the cyclical nature of time, and a hierarchical multi-scale temporal attention
mechanism. These scales are determined by a bottom-up clustering algorithm.
Extensive experiments on several real-world datasets show that our XTSFormer
outperforms several baseline methods in prediction performance.

事件预测是基于历史事件序列预测未来事件的时间和类型，本论文提出了针对非规则时间事件数据的跨时空尺度变换器（XTSFormer），包括新颖的基于特征的周期感知时间位置编码（FCPE）和分层多尺度时间注意机制。在多个真实世界数据集上的广泛实验表明，XTSFormer 在预测性能方面优于几种基准方法。