Future-Guided Learning: A Predictive Approach To Enhance Time-Series Forecasting

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 drifts 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 and adapting to shifts in the data distribution by aligning its predictions with actual future outcomes. This feedback loop allows the forecasting model to dynamically adjust its parameters, focusing on persistent features despite changes in the data. 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 48.7% reduction in MSE for forecasting in nonlinear dynamical systems. By incorporating a predictive feedback mechanism adaptable to data drift, Future-Guided Learning advances how deep learning is applied to time-series forecasting. Our code is publicly available atthis https URL.