PA-RNet: Perturbation-Aware Residual Network for Robust Multimodal Time Series Forecasting

In real-world applications, multimodal time-series forecasting faces a key challenge: textual information is often useful but unreliable. Auxiliary texts may contain irrelevant, ambiguous, incomplete, or structurally corrupted content, making direct text integration prone to introducing noisy semantic signals and degrading forecasting performance. Therefore, robust multimodal forecasting requires a model that can exploit useful textual context while suppressing misleading perturbations.To address this challenge, we propose PA-RNet, a perturbation-aware residual network for robust multimodal time-series forecasting. Before cross-modal fusion, PA-RNet refines numerical features and corrects textual representations to mitigate the influence of perturbations while preserving task-relevant information. It then aligns the refined textual context with temporal patterns in the numerical series, improving forecasting robustness under noisy multimodal inputs.We establish the Lipschitz continuity of PA-RNet with respect to textual embeddings and provide theoretical insights into how perturbation-aware refinement can reduce expected prediction error. We further assess its robustness through supplementary experiments with injected textual perturbations. Results across diverse domains demonstrate that PA-RNet consistently outperforms state-of-the-art baselines while maintaining stable forecasting performance under both original and perturbed textual conditions.