mlsanityData to 7 Oct 2026

EBES: Easy Benchmarking for Event Sequences

arXiv 2410.03399 ↗First posted 4 Oct 2024cs.LG cs.AIcited by 6

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Abstract (arXiv)

Event Sequences (EvS) refer to sequential data characterized by irregular sampling intervals and a mix of categorical and numerical features. Accurate classification of these sequences is crucial for various real-life applications, including healthcare, finance, and user interaction. Despite the popularity of the EvS classification task, there is currently no standardized benchmark or rigorous evaluation protocol. This lack of standardization makes it difficult to compare results across studies, which can result in unreliable conclusions and hinder progress in the field. To address this gap, we present EBES, a comprehensive benchmark for EvS classification with sequence-level targets. EBES features standardized evaluation scenarios and protocols, along with an open-source PyTorch library that implements 9 modern models. Additionally, it includes the largest collection of EvS datasets, featuring 10 curated datasets, including a novel synthetic dataset and real-world data with the largest publicly available banking dataset. The library offers user-friendly interfaces for integrating new methods and datasets. Our benchmarking results highlight the unique properties of EvS compared to other sequential data types, provide a performance ranking of modern models with GRU-based models achieving the best results and reveal the challenges associated with robust EvS learning. The goal of EBES is to facilitate reproducible research, expedite progress in the field, and increase the real-world impact of EvS classification techniques.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 29 of its 76 references that are in the map. First posted on arXiv 4 Oct 2024.

Builds on

29 of 76 references.

References · 29

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17 more
  1. 2019Intensity-Free Learning of Temporal Point Processescited by 138
  2. 2019Set Functions for Time Seriescited by 137
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

  1. 2025HT-Transformer: Event Sequences Classification by Accumulating Prefix Information with History Tokenscited by 3
  2. 2025LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clientscited by 2
  3. 2025Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Taskscited by 1
  4. 2025Towards Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospectscited by 0
  5. 2025How to model Human Actions distribution with Event Sequence Datacited by 0
  6. 2026SOHET: Sequence Of Heterogeneous Events Transformer with Self-Supervised Pre-Trainingcited by 0