FinTime-Fuse: a multimodal LLM for aligning news and financial time-series in enhanced forecasting

Accurate financial time series forecasting hinges on effectively integrating both numerical price data and external textual signals, such as news events. While Time-LLM provides a cross-modal time-series prediction mechanism for price sequences, our main contribution focuses on a novel method to fuse these time-series embeddings with learned representations of news text within a large language model (LLM) framework. In contrast to traditional approaches, often relying on text-only training or simple numeric similarity for retrieval, our method leverages the LLM’s capacity to capture intricate correlations between language-based context and price fluctuations. To facilitate this, we introduce aligned datasets that pair daily (or periodic) financial news with the corresponding market data, ensuring temporal consistency. Our model performs token-level concatenation of textual embeddings extracted from a domain-specific financial language model and patched numerical embeddings, followed by frozen LLaMA self-attention in a shared embedding space. By jointly encoding textual and numerical features, the model captures interactions between historical market patterns and temporally aligned textual context. We evaluate the approach on three datasets spanning gold prices, foreign exchange rates, and Bitcoin, which include structured macroeconomic-calendar descriptions and news-derived textual summaries. Experimental results show that the incorporation of aligned textual information improves the accuracy of forecasting, underscoring the importance of robust integration of news data in financial prediction tasks.