Gamallo Fernández, PedroRama Maneiro, EfrénVidal Aguiar, Juan CarlosLama Penín, Manuel2025-06-182025-06-182024-04-15SoftwareX Volume 26, May 2024, 1017342352-7110https://hdl.handle.net/10347/42124Predictive process monitoring is a subfield of process mining that focuses on predicting the future behavior of real-world processes, anticipating constraint violations and bottlenecks, and enabling real-time decision making. Among other machine learning approaches, Deep Learning-based architectures have achieved high levels of prediction accuracy, becoming an increasingly prolific area of research in recent years. However, the variety of datasets, learning techniques, and metrics used makes the comparison of proposals complicated and biased. To address this problem this paper presents VERONA, a Python library designed for the development of the deep learning predictive process monitoring pipeline. Additionally, this library provides a framework for replicating the experimental setup of the state-of-the-art benchmark in the field, enabling streamlined comparison of new approaches and improving the reproducibility of experiments.eng© 2024 The Author(s). Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Process miningPredictive process monitoringBenchmarkingDeep learning3304 Tecnología de los ordenadoresjournal article10.1016/j.softx.2024.101734open access