檢索增強型大型語言模型用於金融時間序列預測

Retrieval-augmented Large Language Models for Financial Time Series Forecasting

February 9, 2025

作者: Mengxi Xiao, Zihao Jiang, Lingfei Qian, Zhengyu Chen, Yueru He, Yijing Xu, Yuecheng Jiang, Dong Li, Ruey-Ling Weng, Min Peng, Jimin Huang, Sophia Ananiadou, Qianqian Xie

cs.AI

摘要

股票走勢預測是金融時間序列預測中的基本任務，需要從大量時間序列數據中識別和檢索關鍵影響因素。然而，現有的基於文本訓練或數值相似度的檢索方法在處理複雜的金融分析時存在不足。為了應對這一挑戰，我們提出了第一個用於金融時間序列預測的檢索增強生成（RAG）框架，具有三個關鍵創新：以精細調校的10億參數大型語言模型（StockLLM）作為基礎、利用LLM反饋的新型候選選擇方法，以及最大化查詢與歷史重要序列之間相似性的訓練目標。這使我們的檢索器FinSeer能夠發現有意義的模式，同時最小化複雜金融數據中的噪音。我們還構建了集成金融指標和歷史股價的新數據集，用於訓練FinSeer並確保堅固的評估。實驗結果表明，我們的RAG框架優於單獨的StockLLM和隨機檢索，突出其有效性，而FinSeer超越現有的檢索方法，在BIGDATA22上實現了8％更高的準確性並檢索到更具影響力的序列。這項工作強調了金融預測中定制檢索模型的重要性，並為未來研究提供了一個新的框架。

English

Stock movement prediction, a fundamental task in financial time-series forecasting, requires identifying and retrieving critical influencing factors from vast amounts of time-series data. However, existing text-trained or numeric similarity-based retrieval methods fall short in handling complex financial analysis. To address this, we propose the first retrieval-augmented generation (RAG) framework for financial time-series forecasting, featuring three key innovations: a fine-tuned 1B parameter large language model (StockLLM) as the backbone, a novel candidate selection method leveraging LLM feedback, and a training objective that maximizes similarity between queries and historically significant sequences. This enables our retriever, FinSeer, to uncover meaningful patterns while minimizing noise in complex financial data. We also construct new datasets integrating financial indicators and historical stock prices to train FinSeer and ensure robust evaluation. Experimental results demonstrate that our RAG framework outperforms bare StockLLM and random retrieval, highlighting its effectiveness, while FinSeer surpasses existing retrieval methods, achieving an 8\% higher accuracy on BIGDATA22 and retrieving more impactful sequences. This work underscores the importance of tailored retrieval models in financial forecasting and provides a novel framework for future research.