ZODIAC—ZERO-INFLATED OVERSHOOT CONTROLLED DUAL-HEAD INTEGRATION FOR ASYMMETRIC CROSS-DOMAIN FORECASTING

Foundation models promise zero-shot forecasting across domains, yet their effectiveness for cold-start scenarios with zero-inflated distributions remains underexplored. We study cross-domain demand forecasting, predicting outcomes for items launching in new domains without historical data where a substantial fraction of launches ($\approx 30\%$) yield zero outcomes and overestimation carries asymmetric costs. We propose a specialized architecture---ZODIAC---combining: (1) dual-domain temporal integration via stacked recurrent layers processing source and target domain signals, (2) a dual-head design with classifier and regressor explicitly modeling zero-inflated distributions, and (3) an asymmetric loss function penalizing overestimation to align with domain-specific costs. We benchmark our approach against a pretrained in-context learner (TabPFN), an AutoML ensemble (AutoGluon), and a neural time-series model (Temporal Fusion Transformer) across six cross-domain forecasting tasks. Our model achieves 80\% WAPE, a 13\% relative improvement over TabPFN, 25\% over AutoGluon, and 26\% over TFT while reducing systematic overprediction from 66--87\% to under 41\%, a property unachievable with models lacking loss customization.