  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.
