EasyTPP: Towards Open Benchmarking Temporal Point Processes

TL;DR

EasyTPP is a comprehensive, open-source benchmark repository for temporal point processes, facilitating standardized evaluation, comparison, and reproducibility to accelerate research and application in event sequence modeling.

Contribution

It introduces the first centralized benchmark platform for TPPs, including datasets, evaluation tools, and model implementations, promoting reproducibility and collaborative research.

Findings

Provides a unified interface for datasets and models

Includes diverse evaluation programs for reproducibility

Offers implementations of popular neural TPPs and modular components

Abstract

Continuous-time event sequences play a vital role in real-world domains such as healthcare, finance, online shopping, social networks, and so on. To model such data, temporal point processes (TPPs) have emerged as the most natural and competitive models, making a significant impact in both academic and application communities. Despite the emergence of many powerful models in recent years, there hasn't been a central benchmark for these models and future research endeavors. This lack of standardization impedes researchers and practitioners from comparing methods and reproducing results, potentially slowing down progress in this field. In this paper, we present EasyTPP, the first central repository of research assets (e.g., data, models, evaluation programs, documentations) in the area of event sequence modeling. Our EasyTPP makes several unique contributions to this area: a unified interface of using existing datasets and adding new datasets; a wide range of evaluation programs that are easy to use and extend as well as facilitate reproducible research; implementations of popular neural TPPs, together with a rich library of modules by composing which one could quickly build complex models. All the data and implementation can be found at https://github.com/ant-research/EasyTemporalPointProcess. We will actively maintain this benchmark and welcome contributions from other researchers and practitioners. Our benchmark will help promote reproducible research in this field, thus accelerating research progress as well as making more significant real-world impacts.