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

2024-10-19

arXiv.org

AI Summary

To address the dual challenges of modeling long-term temporal dependencies and adapting to data distribution shifts in time-series forecasting, this paper proposes a future-guided dual-model feedback framework grounded in predictive coding. An event detection model identifies critical future states to dynamically steer a forecasting model; their prediction discrepancies constitute “prediction surprises,” which drive end-to-end differentiable parameter updates via an error-weighted mechanism. Crucially, this work pioneers explicit incorporation of future information into the training loop—breaking the conventional unidirectional time-series modeling paradigm—and enables online adaptive calibration. Evaluated on EEG-based epileptic seizure prediction, the method achieves a 44.8% improvement in AUC-ROC; on nonlinear dynamical system forecasting, it reduces MSE by 48.7%. Both gains significantly outperform state-of-the-art time-series models.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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 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 at https://github.com/SkyeGunasekaran/FutureGuidedLearning.

Problem

• Deep Learning
• Long Sequence Analysis
• Data Adaptability

Innovation

• Future-Oriented Learning
• Time Series Prediction
• Error Reduction