DualGround-TS: Addressing Hollowness in Event-Conditioned Time-Series Prediction

Event-conditioned time-series prediction requires models to integrate historical dynamics with textual events, yet correct predictions can arise without dependence on the intended event–history relationship, while plausible explanations can cite evidence that does not support the prediction. We characterize this failure as Hollowness: apparent evidence use does not guarantee behaviorally valid predictive dependence. We introduce DualGround-TS, which addresses prediction and explanation hollowness through two complementary grounding mechanisms. Future-consistent cross-modal grounding uses realized futures to distinguish matched from mismatched event–history states, while behavioral grounding requires explanation-associated temporal and textual evidence to receive corresponding support from prediction behavior. We evaluate DualGround-TS across three complementary regimes: controlled grounding on TimeLitmus, zero-shot contextual forecasting on Context is Key (CiK), and native supervised multimodal forecasting on Time-MMD. On TimeLitmus, DualGround-TS improves all reported metrics in both Finance and Traffic, with its largest gains concentrated on grounding-sensitive diagnostics: relative to Task FT, Event CF-PC improves by 29.6 points, Evidence F1 by 28.9 points, and Avg. CSDR by 15.5 points. On CiK, it reduces zero-shot RCRPS by 14.4% and improves all five context categories. On Time-MMD, it reduces average normalized MSE by 42.6% and achieves lower error in all 9/9 domains. Mechanistic analyses further show stronger selective dependence on predictive evidence and greater matched–mismatched future separation. Together, these results show that DualGround-TS substantially mitigates Hollowness by strengthening both event–history predictive dependence and the behavioral support of explanation-associated evidence, establishing grounded predictive dependence beyond output correctness alone.