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.

External IDs:dblp:conf/ijcai/SakhnoKBSGS25