PyTorch-Lifestream: Learning Embeddings on Discrete Event Sequences
Artem Sakhno, Ivan Kireev, Dmitrii Babaev, Maxim Savchenko, Gleb Gusev, Andrey Savchenko
Abstract
The domain of event sequences is widely applied in various industrial tasks in banking, healthcare, etc., where temporal tabular data processing is required. This paper introduces PyTorch-Lifestream, the first open-source library specially designed to handle event sequences. It supports scenarios with multimodal data and offers a variety of techniques for learning embeddings of event sequences and end-to-end model training. Furthermore, PyTorch-Lifestream efficiently implements state-of-the-art methods for event sequence analysis and adapts approaches from similar domains, thus enhancing the versatility and performance of sequence-based models for a wide range of applications, including financial risk scoring, campaigning, user ID matching, churn prediction, fraud detection, medical diagnostics, and recommender systems.
BibTeX
@inproceedings{ijcai2025_pytorchlifestrea,
title = {PyTorch-Lifestream: Learning Embeddings on Discrete Event Sequences},
author = {Artem Sakhno and Ivan Kireev and Dmitrii Babaev and Maxim Savchenko and Gleb Gusev and Andrey Savchenko},
booktitle = {IJCAI 2025},
year = {2025}
}