用大模型搞定金融数据少时的预测难题，效果比传统方法好。
Large Language Models for Financial Aid in Financial Time-series Forecasting
- 用预训练大模型处理金融数据少、维度高的难题
- 零样本/少样本下仍超越传统方法，准确率提升显著
- 适合金融预测中数据稀缺场景，尤其适合研究者和从业者
由于金融援助（FA）领域历史数据有限且财务信息维度高，金融时间序列预测面临挑战，制约了高效准确模型的发展。本文采用预训练基础模型，结合GPT-2等先进时序模型、Transformer与线性模型，验证其在极少或无需微调情况下，仍可超越传统方法。通过包含金融援助在内的七项时序任务基准测试，证明大语言模型在数据稀缺的金融场景中具备强大潜力。
原文摘要 · Abstract (English)
Considering the difficulty of financial time series forecasting in financial aid, much of the current research focuses on leveraging big data analytics in financial services. One modern approach is to utilize "predictive analysis", analogous to forecasting financial trends. However, many of these time series data in Financial Aid (FA) pose unique challenges due to limited historical datasets and high dimensional financial information, which hinder the development of effective predictive models that balance accuracy with efficient runtime and memory usage. Pre-trained foundation models are employed to address these challenging tasks. We use state-of-the-art time series models including pre-trained LLMs (GPT-2 as the backbone), transformers, and linear models to demonstrate their ability to outperform traditional approaches, even with minimal ("few-shot") or no fine-tuning ("zero-shot"). Our benchmark study, which includes financial aid with seven other time series tasks, shows the potential of using LLMs for scarce financial datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。