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.

翻译：在金融、零售、社交网络和医疗保健等领域具有应用的序列事件预测中，一项关键任务是在指定时间范围内预测多个未来事件。传统上，这一问题通过使用下一事件预测模型（如标记时间点过程）的自回归生成方法来解决。然而，自回归方法使用其自身输出进行未来预测，随着预测时长的延伸，预测质量可能下降。本文通过引入一个新颖的基准测试HoTPP来挑战传统方法，该基准专门设计用于评估模型在特定时间范围内预测事件序列的能力。该基准采用了一种受计算机视觉中目标检测启发的新度量标准，以解决现有度量在评估具有不精确时间步预测的模型时的局限性。我们在多个经典数据集上使用不同模型的评估表明，下一事件预测的高精度并不必然转化为优越的时程预测能力，反之亦然。HoTPP旨在成为开发更鲁棒的事件序列预测方法的有价值工具，最终为该领域的进一步发展铺平道路。