A financial multivariate time series forecasting framework incorporating large language models and experimental evaluation

Mainstream time series forecasting methods based on large language model (LLM) are analyzed, and a unified model framework is proposed to evaluate empirically exchange rate and stock index data. LLM exhibits certain performance advantages in financial time series forecasting, with certain notable limitations． Relying solely on simple textual inputs or prompts may not lead to performance improvements．