Residual TPP: A Unified Lightweight Approach for Event Stream Data Analysis
Authors: Ruoxin Yuan, Guanhua Fang
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate that Residual TPP consistently achieves state-of-the-art goodness-of-fit and prediction performance in multiple domains and offers significant computational advantages as well. |
| Researcher Affiliation | Academia | 1School of Management, Fudan University, Shanghai, China. Correspondence to: Guanhua Fang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 The Overall Pseudocode of Residual TPP |
| Open Source Code | Yes | Our code is publicly available at https://github.com/ruoxinyuan/Residual TPP. |
| Open Datasets | Yes | Datasets. We evaluate our method on six real-world benchmark datasets: MIMIC-II (Johnson et al., 2016), Retweet (Zhou et al., 2013), Earthquake (Xue et al., 2024), Stack Overflow (Leskovec & Krevl, 2014), Amazon (Ni, 2018) and Volcano (Xue et al., 2024). The MIMIC-II dataset is available at the public Git Hub repository1, and the others can be accessed via the Easy TPP library2. 1https://github.com/hongyuanmei/neurawkes 2https://github.com/ant-research/ Easy Temporal Point Process |
| Dataset Splits | Yes | Each dataset is split into training, validation, and test sets. The validation set is for hyperparameter tuning, and the test set is for model evaluation. Table 7. Statistics of the real-world datasets. The table columns, from left to right, represent the dataset, number of event types, number of events, sequence length, and number of sequences. DATASET # TYPES # EVENTS SEQUENCE LENGTH # SEQUENCES MIN MEAN MAX TRAIN VALID TEST MIMIC-II 75 2,419 2 4 33 527 58 65 Table 8. Statistics of the synthetic datasets. ... DATASET # TYPES # EVENTS # RESIDUALS SEQUENCE LENGTH # SEQUENCES MIN MEAN MAX TRAIN VALID TEST POSSION-BASED 5 33,243 4,253 20 29 100 600 200 200 |
| Hardware Specification | No | All training was conducted on a CPU. |
| Software Dependencies | No | All experiments in this study were implemented using Py Torch (Paszke et al., 2019), trained using the Adam (Kingma, 2014) optimizer, and executed on a CPU. In our experiments, we implement this using Tick3 (Bacry et al., 2017). |
| Experiment Setup | No | For simplicity, we model the event stream data using a Hawkes process with a fixed baseline intensity and an exponential decay function to capture the self-excitation property: λ(1) k (t) = µk + X i:ti<t αk,kiβk,kie βk,ki(t ti). All experiments in this study were implemented using Py Torch (Paszke et al., 2019), trained using the Adam (Kingma, 2014) optimizer, and executed on a CPU. In all experiments presented in this paper, we set ρ1 = ρ2 = 1. The parameters a and b are adjusted based on the characteristics of the data from different datasets to ensure that the proportion of residual events remains within an appropriate range. |