EBES Easy Benchmarking for Event Sequences.

🎉 Accepted at KDD 2025!

Our paper "EBES: Easy Benchmarking for Event Sequences" has been accepted to the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '25).

@inproceedings{osin2025ebes,
  title={EBES: Easy Benchmarking for Event Sequences},
  author={Osin, Dmitry and Udovichenko, Igor and Shvetsov, Egor and Moskvoretskii, Viktor and Burnaev, Evgeny},
  booktitle={Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2},
  pages={5730--5741},
  year={2025}
}

EBES is an easy-to-use development and application toolkit for Event Sequence(EvS) Assesment, with key features in configurability, compatibility and reproducibility. We hope this project could benefit both researchers and practitioners with the goal of easily customized development and open benchmarking in EvS.

Setup

Installation

To install the latest stable version:

pip install ebes

Datasets

Usage

python main -d age -m gru -e correlation -s best

Results:

Performance of various models as a function of number of sequences. Metrics from Table 1 are reported. Number of sequences is presented in log scale. Standard deviation across 3 runs is depicted as vertical lines.

Performance metric relationships and correlations of different subsets among all methods on PhysioNet2012 are presented. We do not observe a correlation between the test metric and train-val on PhysioNet2012, as seen in the right upper corner. For the Taobao dataset, we do not observe a clear linear trend between hpo-val and the test metric suggesting the presence of distribution shift.