In sequential event prediction, which finds applications in finance, retail,
social networks, and healthcare, a crucial task is forecasting multiple future
events within a specified time horizon. Traditionally, this has been addressed
through autoregressive generation using next-event prediction models, such as
Marked Temporal Point Processes. However, autoregressive methods use their own
output for future predictions, potentially reducing quality as the prediction
horizon extends. In this paper, we challenge traditional approaches by
introducing a novel benchmark, HoTPP, specifically designed to evaluate a
model's ability to predict event sequences over a horizon. This benchmark
features a new metric inspired by object detection in computer vision,
addressing the limitations of existing metrics in assessing models with
imprecise time-step predictions. Our evaluations on established datasets
employing various models demonstrate that high accuracy in next-event
prediction does not necessarily translate to superior horizon prediction, and
vice versa. HoTPP aims to serve as a valuable tool for developing more robust
event sequence prediction methods, ultimately paving the way for further
advancements in the field.

通过引入新的 HoTPP 基准和度量方法，这篇论文挑战传统方法，评估模型在时间范围内预测事件序列的能力，并且证明下一个事件预测的高准确性不一定能转化为更好的时间范围预测，为进一步改进这一领域的方法铺平道路。