A predictive approach to enhance time-series forecasting

Skye Gunasekaran, Assel Kembay, Hugo Ladret, Rui-Jie Zhu, Laurent Perrinet, Omid Kavehei and Jason Eshraghian ()
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Skye Gunasekaran: University of California
Assel Kembay: University of California
Hugo Ladret: Friedrich Miescher Institute for Biomedical Research
Rui-Jie Zhu: University of California
Laurent Perrinet: Aix Marseille Univ, CNRS
Omid Kavehei: The University of Sydney
Jason Eshraghian: University of California

Nature Communications, 2025, vol. 16, issue 1, 1-7

Abstract: Abstract Accurate time-series forecasting is crucial in various scientific and industrial domains, yet deep learning models often struggle to capture long-term dependencies and adapt to data distribution shifts over time. We introduce Future-Guided Learning, an approach that enhances time-series event forecasting through a dynamic feedback mechanism inspired by predictive coding. Our method involves two models: a detection model that analyzes future data to identify critical events and a forecasting model that predicts these events based on current data. When discrepancies occur between the forecasting and detection models, a more significant update is applied to the forecasting model, effectively minimizing surprise, allowing the forecasting model to dynamically adjust its parameters. We validate our approach on a variety of tasks, demonstrating a 44.8% increase in AUC-ROC for seizure prediction using EEG data, and a 23.4% reduction in MSE for forecasting in nonlinear dynamical systems (outlier excluded). By incorporating a predictive feedback mechanism, Future-Guided Learning advances how deep learning is applied to time-series forecasting.

Date: 2025
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DOI: 10.1038/s41467-025-63786-4

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