Koopman Ensembles for Probabilistic Time Series Forecasting

arXiv:2403.06757v1 Announce Type: new
Abstract: 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 …

abstract arxiv context cs.lg data data-driven fields forecasting however knowledge machine machine learning predictions series systems time series time series forecasting type uncertainty vast