LLM-GC: Advancing Granger Causal Discovery from Time Series with Multimodel Language Modeling

Recent advances in neural Granger causal methods have shown promise in modeling temporal nonlinear dependencies. However, existing approaches remain confined to raw time-series data, inherently lacking contextual semantics and tending to overfit, which undermines their real-world applicability. To address these challenges, we propose LLM-GC, a novel LLM-empowered multimodal Granger causality discovery framework that enriches unimodal temporal dynamics with semantic priors and world knowledge distilled from large language models (LLMs). LLM-GC leverages dual-modality encoding to capture and align temporal and contextual dynamics by Cross-Modal Dual Retrieval while avoiding causal entanglement across modalities. To extract multimodal causal features, we introduce a causality-aware self-attention mechanism by simply inverting the conventional self-attention structure, enabling a shared causality augmenter to effectively highlight consistent causal patterns across modalities. LLM-GC is the first to bridge LLMs and Granger causality, and experiments on synthetic and real-world benchmark datasets demonstrate that LLM-GC outperforms existing state-of-the-art methods in Granger causal discovery.