# Volvo Discovery Challenge at ECML-PKDD 2024

arXiv: https://arxiv.org/abs/2409.11446 · HTML: https://mlsanity.com/paper/2409.11446/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Time-Transformer: Integrating Local and Global Features for Better Time Series Generation (Extended Version)](https://mlsanity.com/paper/2312.11714/) (2023)
- [Increasing biases can be more efficient than increasing weights](https://mlsanity.com/paper/2301.00924/) (2023)
- [Transformers with Stochastic Competition for Tabular Data Modelling](https://mlsanity.com/paper/2407.13238/) (2024)

## Built on by (most cited first, 2 of 2)

- [Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024](https://mlsanity.com/paper/2409.13877/) (2024)
- [Colliding with Adversaries at ECML-PKDD 2025 Model Robustness Competition 1st Prize Solution](https://mlsanity.com/paper/2510.16443/) (2025)

## Abstract (arXiv)

This paper presents an overview of the Volvo Discovery Challenge, held during the ECML-PKDD 2024 conference. The challenge's goal was to predict the failure risk of an anonymized component in Volvo trucks using a newly published dataset. The test data included observations from two generations (gen1 and gen2) of the component, while the training data was provided only for gen1. The challenge attracted 52 data scientists from around the world who submitted a total of 791 entries. We provide a brief description of the problem definition, challenge setup, and statistics about the submissions. In the section on winning methodologies, the first, second, and third-place winners of the competition briefly describe their proposed methods and provide GitHub links to their implemented code. The shared code can be interesting as an advanced methodology for researchers in the predictive maintenance domain. The competition was hosted on the Codabench platform.