MulTiCast: A Multimodal Time Series Forecasting System

Abstract Time-series forecasting plays an essential role in domains such as finance, healthcare, and energy. Yet most existing systems operate in a unimodal setting, overlooking complementary information available from visual and textual modalities. There are surprisingly few time-series forecasting demos, and multimodal, interpretable demos are rarer still. In practice, it is difficult for users to experiment with foundation models on their own time-series data due to strict input requirements, heavy setup burdens, and limited interpretability support. We present MulTiCast, an interactive Multimodal Time series foreCasting system that enables users to combine numerical signals with visual and textual context to improve predictions. The system builds on pretrained models with lightweight adaptation, but its central contribution lies in the interactive demonstration platform. Through a web interface via Hugging Face Spaces, users can load datasets, toggle modality inclusion, and visualize forecasts together with the attention maps of each modality, providing insights into the reasoning path behind the predictions.