XTSFormer: Cross-Temporal-Scale Transformer for Irregular Time Event
  Prediction
Tingsong Xiao Zelin Xu Wenchong He Jim Su Yupu Zhang Raymond Opoku Ronald Ison Jason Petho Jiang Bian Patrick Tighe Parisa Rashidi Zhe Jiang
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.