language, pipeline_tag, library_name, extra_gated_fields

language	pipeline_tag	library_name	extra_gated_fields
en	image-text-to-text	transformers	First name Last name Affiliation Job title Email Country I want to use this model for I agree to use this model for non-commercial use ONLY text text text text text country type options select Research Education label value Other other checkbox

FinSeer Model Card

Model details

Model type: StockLLM is an open-source fine-tuned 1B large language model as the backbone of our first retrieval-augmented generation (RAG) framework specifically designed for financial time-series forecasting.

Paper or resources for more information:

https://arxiv.org/pdf/2502.05878

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}, 
}

---

license: mit