Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling continuous-time event sequences, such as user activities on the web and financial transactions. In real-world applications, event data is typically received in a \emph{streaming} manner, where the distribution of patterns may shift over time. Additionally, \emph{privacy and memory constraints} are commonly observed in practical scenarios, further compounding the challenges. Therefore, the continuous monitoring of a TPP to learn the streaming event sequence is an important yet under-explored problem. Our work paper addresses this challenge by adopting Continual Learning (CL), which makes the model capable of continuously learning a sequence of tasks without catastrophic forgetting under realistic constraints. Correspondingly, we propose a simple yet effective framework, PromptTPP\footnote{Our code is available at {\small \url{ https://github.com/yanyanSann/PromptTPP}}}, by integrating the base TPP with a continuous-time retrieval prompt pool. The prompts, small learnable parameters, are stored in a memory space and jointly optimized with the base TPP, ensuring that the model learns event streams sequentially without buffering past examples or task-specific attributes. We present a novel and realistic experimental setup for modeling event streams, where PromptTPP consistently achieves state-of-the-art performance across three real user behavior datasets.

翻译：神经时间点过程是建模连续时间事件序列的主流框架，例如网络用户活动与金融交易等场景。在实际应用中，事件数据通常以流式方式接收，其模式分布会随时间发生偏移。此外，隐私与内存约束在实际场景中普遍存在，进一步加剧了建模挑战。因此，持续监测时间点过程以学习流式事件序列是一项重要但尚未充分探索的问题。本文通过引入持续学习应对这一挑战，该方法使模型在现实约束下能够持续学习任务序列而不发生灾难性遗忘。相应地，我们提出一个简洁而高效的框架——PromptTPP\footnote{代码开源于: {\small \url{https://github.com/yanyanSann/PromptTPP}}}，将基础时间点过程与连续时间检索提示池相结合。提示作为可学习的小参数存储在记忆空间中，与基础时间点过程联合优化，确保模型无需缓冲历史样本或任务特定属性即可顺序学习事件流。我们设计了一种新颖且贴近实际的实验设置来建模事件流，在三个真实用户行为数据集上，PromptTPP持续实现了最先进的性能。