Forecasting future events over extended periods, known as long-horizon prediction, is a fundamental task in various domains, including retail, finance, healthcare, and social networks. Traditional methods, such as Marked Temporal Point Processes (MTPP), typically use autoregressive models to predict multiple future events. However, these models frequently encounter issues such as converging to constant or repetitive outputs, which significantly limits their effectiveness and applicability. To overcome these limitations, we propose DeTPP (Detection-based Temporal Point Processes), a novel approach inspired by object detection methods from computer vision. DeTPP utilizes a novel matching-based loss function that selectively focuses on reliably predictable events, enhancing both training robustness and inference diversity. Our method sets a new state-of-the-art in long-horizon event prediction, significantly outperforming existing MTPP and next-K approaches. The implementation of DeTPP is publicly available on GitHub.

翻译：在零售、金融、医疗和社交网络等多个领域中，对未来事件进行长期预测（即长时程预测）是一项基础性任务。传统方法，如标记时间点过程（MTPP），通常使用自回归模型来预测多个未来事件。然而，这些模型经常遇到收敛到恒定或重复输出等问题，这极大地限制了其有效性和适用性。为了克服这些限制，我们提出了DeTPP（基于检测的时间点过程），这是一种受计算机视觉中目标检测方法启发的新方法。DeTPP采用了一种新颖的基于匹配的损失函数，该函数有选择地关注可可靠预测的事件，从而增强了训练的鲁棒性和推理的多样性。我们的方法在长时程事件预测中确立了新的最先进水平，显著优于现有的MTPP和next-K方法。DeTPP的实现已在GitHub上公开提供。