Bin Yang

Forget relying solely on numbers: VoT unlocks richer time series forecasts by fusing LLM reasoning over event-related text with multi-level data alignment.

By explicitly aligning the graph structures of predicted and actual time series correlations, GCGNet achieves superior forecasting accuracy in noisy, real-world datasets compared to methods that model temporal and channel dependencies separately.