Koopman Ensembles for Probabilistic Time Series Forecasting - Equipe Observations Signal & Environnement Access content directly
Conference Papers Year : 2024

Koopman Ensembles for Probabilistic Time Series Forecasting


In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently been proposed. However, the vast majority of those works are limited to deterministic predictions, while the knowledge of uncertainty is critical in fields like meteorology and climatology. In this work, we investigate the training of ensembles of models to produce stochastic outputs. We show through experiments on real remote sensing image time series that ensembles of independently trained models are highly overconfident and that using a training criterion that explicitly encourages the members to produce predictions with high inter-model variances greatly improves the uncertainty quantification of the ensembles.
Fichier principal
Vignette du fichier
2024031290100_342670_1531.pdf (284.16 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04499908 , version 1 (11-03-2024)
hal-04499908 , version 2 (13-03-2024)




Anthony Frion, Lucas Drumetz, Guillaume Tochon, Mauro Dalla Mura, Abdeldjalil Aissa El Bey. Koopman Ensembles for Probabilistic Time Series Forecasting. EUSIPCO 2024 - 32nd European Signal Processing Conference, EURASIP, Aug 2024, Lyon, France. ⟨hal-04499908v2⟩
433 View
111 Download



Gmail Mastodon Facebook X LinkedIn More