temporal-point-processes

这篇论文将事件序列预测重新表述为条件生成建模问题，提出了一个同时衡量预测准确性和分布真实性的基准，并引入Wasserstein BaryBooster算法，将扩散模型的真实感转化为预测精度。

This paper introduces a screen-and-confirm protocol to certify whether conditioning signals improve temporal point process models of customer return timing, finding that continuous-time decay is nearly sufficient and added conditioning is redundant or harmful.

This paper introduces SigTPP, the first signature-based generative model for temporal point processes, which uses a pathwise framework and interarrival embedding to overcome limitations of likelihood-based methods, and demonstrates strong empirical performance across synthetic and real-world datasets.

This paper introduces Monotone Alternating Splines (MAS), a novel framework for modeling cumulative conditional intensity functions in temporal point processes. MAS overcomes structural limitations of existing monotone neural networks, achieving better accuracy and efficiency on synthetic and real datasets.

Introduces a semi-autoregressive framework that combines latent block diffusion with temporal point processes for generating asynchronous event sequences, reducing error accumulation while enabling variable-length output.