Paper ID: 2402.02258

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.

Submitted: Feb 3, 2024