# Cumulative Distribution Function based General Temporal Point Processes

arXiv: https://arxiv.org/abs/2402.00388 · HTML: https://mlsanity.com/paper/2402.00388/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [Transformer Hawkes Process](https://mlsanity.com/paper/2002.09291/) (2020)
- [Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling](https://mlsanity.com/paper/2207.04179/) (2022)
- [Fully Neural Network based Model for General Temporal Point Processes](https://mlsanity.com/paper/1905.09690/) (2019)
- [Neural Temporal Point Processes: A Review](https://mlsanity.com/paper/2104.03528/) (2021)
- [Wasserstein Learning of Deep Generative Point Process Models](https://mlsanity.com/paper/1705.08051/) (2017)
- [Learning Temporal Point Processes via Reinforcement Learning](https://mlsanity.com/paper/1811.05016/) (2018)
- [Deep Reinforcement Learning of Marked Temporal Point Processes](https://mlsanity.com/paper/1805.09360/) (2018)
- [Neural Likelihoods via Cumulative Distribution Functions](https://mlsanity.com/paper/1811.00974/) (2018)
- [Modeling Event Propagation via Graph Biased Temporal Point Process](https://mlsanity.com/paper/1908.01623/) (2019)
- [Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information](https://mlsanity.com/paper/1906.08952/) (2019)
- [Meta Temporal Point Processes](https://mlsanity.com/paper/2301.12023/) (2023)
- [VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media](https://mlsanity.com/paper/2110.15454/) (2021)
- [Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences](https://mlsanity.com/paper/2202.11485/) (2022)
- [Distilling Information Reliability and Source Trustworthiness from Digital Traces](https://mlsanity.com/paper/1610.07472/) (2016)

## Built on by (most cited first, 1 of 1)

- [Efficient Temporal Point Processes via Monotone Alternating Splines](https://mlsanity.com/paper/2607.01752/) (2026)

## Abstract (arXiv)

Temporal Point Processes (TPPs) hold a pivotal role in modeling event sequences across diverse domains, including social networking and e-commerce, and have significantly contributed to the advancement of recommendation systems and information retrieval strategies. Through the analysis of events such as user interactions and transactions, TPPs offer valuable insights into behavioral patterns, facilitating the prediction of future trends. However, accurately forecasting future events remains a formidable challenge due to the intricate nature of these patterns. The integration of Neural Networks with TPPs has ushered in the development of advanced deep TPP models. While these models excel at processing complex and nonlinear temporal data, they encounter limitations in modeling intensity functions, grapple with computational complexities in integral computations, and struggle to capture long-range temporal dependencies effectively. In this study, we introduce the CuFun model, representing a novel approach to TPPs that revolves around the Cumulative Distribution Function (CDF). CuFun stands out by uniquely employing a monotonic neural network for CDF representation, utilizing past events as a scaling factor. This innovation significantly bolsters the model's adaptability and precision across a wide range of data scenarios. Our approach addresses several critical issues inherent in traditional TPP modeling: it simplifies log-likelihood calculations, extends applicability beyond predefined density function forms, and adeptly captures long-range temporal patterns. Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction, and empirical validation of CuFun's effectiveness through extensive experimentation on synthetic and real-world datasets.