Temporal Point Processes (TPPs) serve as the standard mathematical framework for modeling asynchronous event sequences in continuous time. However, classical TPP models are often constrained by strong assumptions, limiting their ability to capture complex real-world event dynamics. To overcome this limitation, researchers have proposed Neural TPPs, which leverage neural network parametrizations to offer more flexible and efficient modeling. While recent studies demonstrate the effectiveness of Neural TPPs, they often lack a unified setup, relying on different baselines, datasets, and experimental configurations. This makes it challenging to identify the key factors driving improvements in predictive accuracy, hindering research progress. To bridge this gap, we present a comprehensive large-scale experimental study that systematically evaluates the predictive accuracy of state-of-the-art neural TPP models. Our study encompasses multiple real-world and synthetic event sequence datasets, following a carefully designed unified setup. We thoroughly investigate the influence of major architectural components such as event encoding, history encoder, and decoder parametrization on both time and mark prediction tasks. Additionally, we delve into the less explored area of probabilistic calibration for neural TPP models. By analyzing our results, we draw insightful conclusions regarding the significance of history size and the impact of architectural components on predictive accuracy. Furthermore, we shed light on the miscalibration of mark distributions in neural TPP models. Our study aims to provide valuable insights into the performance and characteristics of neural TPP models, contributing to a better understanding of their strengths and limitations.

翻译：时间点过程是建模连续时间中异步事件序列的标准数学框架。然而，经典的时间点过程模型往往受限于强假设，难以捕捉复杂真实世界的事件动态。为克服这一局限，研究者提出了神经时间点过程，通过利用神经网络参数化实现更灵活高效的建模。尽管近期研究证明了神经时间点过程的有效性，但这些研究常缺乏统一设置，依赖不同基线、数据集和实验配置，导致难以识别提升预测准确性的关键因素，阻碍了研究进展。为弥合这一差距，我们开展了一项全面的大规模实验研究，系统评估了最先进的神经时间点过程模型的预测准确性。本研究涵盖多个真实世界和合成事件序列数据集，遵循精心设计的统一实验设置。我们深入探究了事件编码、历史编码器和解码器参数化等主要架构组件对时间预测和标记预测任务的影响。此外，我们还探讨了神经时间点过程模型在概率校准方面尚未充分研究的领域。通过分析实验结果，我们得出了关于历史长度重要性以及架构组件对预测准确性影响的深刻结论。同时，我们揭示了神经时间点过程模型中标记分布的错误校准问题。本研究旨在为神经时间点过程模型的性能与特征提供宝贵见解，加深对其优势与局限性的理解。