Finseer

🚀 FinSeer Model Card

The first dedicated retriever for financial time - series forecasting, offering research - based solutions.

🚀 Quick Start

This guide will introduce the FinSeer model, including its details, intended use, installation steps, disclaimer, and citation information.

✨ Features

• Dedicated Retriever: FinSeer is the first dedicated retriever for financial time - series forecasting.
• RAG Framework: It is designed for research on financial time - series forecasting using the retrieval - augmented generation (RAG) framework.

📦 Installation

You can install the necessary packages using the following commands:

pip install InstructorEmbedding
pip install -U FlagEmbedding
pip install sentence-transformers==2.2.2
pip install protobuf==3.20.0
pip install yahoo-finance
python -m pip install -U angle-emb
pip install transformers==4.33.2  # UAE

📚 Documentation

Model details

Intended use

The primary use of FinSeer is research on financial time - series forecasting using retrieval - augmented generation (RAG) framework.

📄 License

Disclaimer

This repository and its contents are provided for academic and educational purposes only. None of the material constitutes financial, legal, or investment advice. No warranties, express or implied, are offered regarding the accuracy, completeness, or utility of the content. The authors and contributors are not responsible for any errors, omissions, or any consequences arising from the use of the information herein. Users should exercise their own judgment and consult professionals before making any financial, legal, or investment decisions. The use of the software and information contained in this repository is entirely at the user's own risk.

By using or accessing the information in this repository, you agree to indemnify, defend, and hold harmless the authors, contributors, and any affiliated organizations or persons from any and all claims or damages.

Citaion

@misc{xiao2025enhancingfinancialtimeseriesforecasting,
      title={Enhancing Financial Time-Series Forecasting with Retrieval-Augmented Large Language Models}, 
      author={Mengxi Xiao and Zihao Jiang and Lingfei Qian and Zhengyu Chen and Yueru He and Yijing Xu and Yuecheng Jiang and Dong Li and Ruey-Ling Weng and Min Peng and Jimin Huang and Sophia Ananiadou and Qianqian Xie},
      year={2025},
      eprint={2502.05878},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.05878}, 
}