Learning from History: A Retrieval-Augmented Framework for Spatiotemporal Prediction

2025-10-28

AI Summary

Long-term spatiotemporal forecasting of complex physical systems remains challenged by error accumulation and physical inconsistency. To address this, we propose Retrieval-Augmented Prediction (RAP), the first framework to incorporate historical evolution segments as non-parametric dynamic priors into the forecasting pipeline: it retrieves realistic trajectory snippets via similarity-based state retrieval and conditions the prediction model on them—rather than merely enforcing them as loss constraints—within a dual-stream neural architecture that jointly optimizes data-driven accuracy and physical consistency. RAP overcomes fundamental limitations of purely parametric modeling. Evaluated on meteorological forecasting, turbulence simulation, and wildfire propagation, it achieves significant improvements over state-of-the-art methods, notably enhancing long-horizon prediction accuracy, suppressing error divergence, and generating dynamically coherent trajectories that adhere more closely to underlying physical laws.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Accurate and long-term spatiotemporal prediction for complex physical systems remains a fundamental challenge in scientific computing. While deep learning models, as powerful parametric approximators, have shown remarkable success, they suffer from a critical limitation: the accumulation of errors during long-term autoregressive rollouts often leads to physically implausible artifacts. This deficiency arises from their purely parametric nature, which struggles to capture the full constraints of a system's intrinsic dynamics. To address this, we introduce a novel extbf{Retrieval-Augmented Prediction (RAP)} framework, a hybrid paradigm that synergizes the predictive power of deep networks with the grounded truth of historical data. The core philosophy of RAP is to leverage historical evolutionary exemplars as a non-parametric estimate of the system's local dynamics. For any given state, RAP efficiently retrieves the most similar historical analog from a large-scale database. The true future evolution of this analog then serves as a extbf{reference target}. Critically, this target is not a hard constraint in the loss function but rather a powerful conditional input to a specialized dual-stream architecture. It provides strong extbf{dynamic guidance}, steering the model's predictions towards physically viable trajectories. In extensive benchmarks across meteorology, turbulence, and fire simulation, RAP not only surpasses state-of-the-art methods but also significantly outperforms a strong extbf{analog-only forecasting baseline}. More importantly, RAP generates predictions that are more physically realistic by effectively suppressing error divergence in long-term rollouts.

Problem

• Addresses error accumulation in long-term spatiotemporal prediction models
• Enhances physical plausibility of deep learning forecasts using historical data
• Mitigates autoregressive rollout artifacts in complex physical system simulations

Innovation

• Retrieval-Augmented Prediction framework combines deep networks with historical data
• Retrieves similar historical analogs as non-parametric local dynamics estimate
• Uses dual-stream architecture with dynamic guidance from historical evolution