# EBES: Easy Benchmarking for Event Sequences

arXiv: https://arxiv.org/abs/2410.03399 · HTML: https://mlsanity.com/paper/2410.03399/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 76 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)
- [Multi-Time Attention Networks for Irregularly Sampled Time Series](https://mlsanity.com/paper/2101.10318/) (2021)
- [Transformer Hawkes Process](https://mlsanity.com/paper/2002.09291/) (2020)
- [GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series](https://mlsanity.com/paper/1905.12374/) (2019)
- [Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding](https://mlsanity.com/paper/2106.00750/) (2021)
- [Neural Jump Stochastic Differential Equations](https://mlsanity.com/paper/1905.10403/) (2019)
- [Intensity-Free Learning of Temporal Point Processes](https://mlsanity.com/paper/1909.12127/) (2019)
- [Set Functions for Time Series](https://mlsanity.com/paper/1909.12064/) (2019)
- [Fully Neural Network based Model for General Temporal Point Processes](https://mlsanity.com/paper/1905.09690/) (2019)
- [Interpolation-Prediction Networks for Irregularly Sampled Time Series](https://mlsanity.com/paper/1909.07782/) (2019)
- [EasyTPP: Towards Open Benchmarking Temporal Point Processes](https://mlsanity.com/paper/2307.08097/) (2023)
- [Closed-form Continuous-time Neural Models](https://mlsanity.com/paper/2106.13898/) (2021)
- [Learning from Irregularly-Sampled Time Series: A Missing Data Perspective](https://mlsanity.com/paper/2008.07599/) (2020)
- [CoLES: Contrastive Learning for Event Sequences with Self-Supervision](https://mlsanity.com/paper/2002.08232/) (2020)
- [Sources of Irreproducibility in Machine Learning: A Review](https://mlsanity.com/paper/2204.07610/) (2022)
- [A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness](https://mlsanity.com/paper/2312.01082/) (2023)
- [HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?](https://mlsanity.com/paper/2406.14341/) (2024)
- [Multimodal Banking Dataset: Understanding Client Needs through Event Sequences](https://mlsanity.com/paper/2409.17587/) (2024)
- [SeqNAS: Neural Architecture Search for Event Sequence Classification](https://mlsanity.com/paper/2401.03246/) (2024)
- [MLEM: Generative and Contrastive Learning as Distinct Modalities for Event Sequences](https://mlsanity.com/paper/2401.15935/) (2024)
- [Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations](https://mlsanity.com/paper/2406.06149/) (2024)
- [Benchmark time series data sets for PyTorch -- the torchtime package](https://mlsanity.com/paper/2207.12503/) (2022)
- [Uniting contrastive and generative learning for event sequences models](https://mlsanity.com/paper/2408.09995/) (2024)

## Built on by (most cited first, 7 of 7)

- [HT-Transformer: Event Sequences Classification by Accumulating Prefix Information with History Tokens](https://mlsanity.com/paper/2508.01474/) (2025)
- [LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients](https://mlsanity.com/paper/2508.10021/) (2025)
- [Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks](https://mlsanity.com/paper/2510.11903/) (2025)
- [Towards Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospects](https://mlsanity.com/paper/2502.04899/) (2025)
- [How to model Human Actions distribution with Event Sequence Data](https://mlsanity.com/paper/2510.05856/) (2025)
- [SOHET: Sequence Of Heterogeneous Events Transformer with Self-Supervised Pre-Training](https://mlsanity.com/paper/2606.21356/) (2026)
- [Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective](https://mlsanity.com/paper/2610.12586/) (2026)

## 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.