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Investigating four deep learning approaches as candidates for unified models in time series forecasting and event prediction: application in anesthesia training

Year: 2025

Type: conference-paper

Abstract: This paper explores deep learning architectures for the purposes of unsupervised representation learning of hybrid asynchronous data, and joint prediction tasks.We aim to forecast short-term multivariate time series contextualized by events and to predict events contextualized by time series.Our proof-of-concept examines a real-world case of digitally assisted training in anesthesia.We evaluate four different architectures, using two strategies to integrate both time series and event sequences in the models.We assess the prediction quality of the models, and demonstrate that only one of the four architectures achieves performance outcomes compatible with our application objective.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-158

Authors: Quentin Victor, Ianis Clavier, Hugo Boisaubert, Fabien Picarougne, Corinne Lejus-Bourdeau, Christine Sinoquet

Institutions: Centre National de la Recherche Scientifique, Laboratoire des Sciences du Numérique de Nantes, Nantes Université

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4409480748

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