Adapting Time Series Foundation Models for Event-Conditioned Multimodal Forecasting

Time series foundation models (TSFMs) have demonstrated strong zero-shot forecasting capabilities through large-scale pretraining on heterogeneous time series data. However, they cannot natively incorporate natural-language descriptions of external events, such as promotional events, holidays, and policy changes, that provide predictive information not recoverable from historical observations alone. Existing work either relies on LLM-based reasoning that does not guarantee accurate numerical forecasts, or requires costly joint multimodal pretraining from scratch. To address this, we propose MM-Time, a parameter-efficient framework that equips pretrained TSFMs with event-aware forecasting through multimodal continued pretraining. To bridge the semantic–effect gap in which semantically distinct events exert identical numerical impacts, we introduce an Event-Effect Prototype Router. It maps event descriptions and temporal attributes to sparse combinations of shared learnable prototypes under forecasting supervision, encouraging the sharing of forecast-relevant information across events. Furthermore, to address temporal alignment over event durations, we propose Event Interval Rotary-Bias Attention, which conditions temporal attention on event boundary positions and incorporates an interval-distance bias to favor temporally relevant events. We adapt three state-of-the-art TSFMs, Chronos-2, Falcon-2, and TiRex-2, and show that MM-Time consistently improves event-conditioned forecasting across multiple downstream datasets.