---
title: "PyTorch-Lifestream: Learning Embeddings on Discrete Event Sequences"
authors:
  - "Artem Sakhno"
  - "Ivan Kireev"
  - "Dmitrii Babaev"
  - "Maxim Savchenko"
  - "Gleb Gusev"
  - "Andrey Savchenko"
doi: "10.24963/ijcai.2025/1272"
url: "https://ijcai.org/proceedings/2025/1272"
pdf: "https://ijcai.org/proceedings/2025/1272.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "11104-11108"
track: "Demo Track"
keywords:
  - "Machine Learning: ML: Self-supervised Learning"
  - "Machine Learning: ML: Benchmarks"
  - "Machine Learning: ML: Sequence and graph learning"
  - "Machine Learning: ML: Unsupervised learning"
---

# PyTorch-Lifestream: Learning Embeddings on Discrete Event Sequences

Artem Sakhno, Ivan Kireev, Dmitrii Babaev, Maxim Savchenko, Gleb Gusev, Andrey Savchenko

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 11104-11108. DOI: [10.24963/ijcai.2025/1272](https://doi.org/10.24963/ijcai.2025/1272)

## 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.