DynaGuard: Reducing Negative Transfer through Controlled Context Injection in Multimodal Time Series Forecasting

In time series forecasting, exogenous text such as news and reports can provide predictive signals not yet reflected in numerical histories, but unreliable, stale, or temporally misaligned context can increase forecasting error relative to a matched numerical-only reference, causing negative transfer. To reduce negative transfer, we address three linked questions: what content to retain, how to assess its temporal relevance, and where and how strongly to apply corrections. We propose DynaGuard, a backbone-compatible framework for controlled context injection: it models target dynamics from numerical histories and uses text to provide corrections governed by these three decisions. Time-Slice Context Compression (TCC) selects and compresses forecasting-relevant content within each historical interval. Context-Adaptive Temporal Bias (CATB) uses these compressed representations to predict salience and decay, guiding retrieval according to historical lag and numerical-token position. Content-Aware Gated Injection (CAGI) then uses the numerical state and retrieved context to control correction strength at each numerical token and feature channel. These stages learn jointly while preserving a direct numerical path to prediction. Our main comparisons cover 16 tasks from three data collections and six numerical backbones, with five random seeds per configuration. DynaGuard reduces negative transfer and improves forecasting accuracy. Ablations support each component's contribution, and the components also benefit other text-integration methods. Code is available at https://anonymous.4open.science/r/DynaGuard2027/.