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title: FinText-TSFM
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FinText-TSFM 📈
Time Series Foundation Models for Finance
🚀 TSFMs Release
We are pleased to introduce FinText-TSFM, a comprehensive suite of time series foundation models (TSFMs) developed for financial forecasting and quantitative research. This release accompanies the paper : Re(Visiting) Time Series Foundation Models in Finance by Eghbal Rahimikia, Hao Ni, and Weiguan Wang (2025).
💡 Key Highlights
Finance-Native Pre-training:
Models are pre-trained from scratch on large-scale financial time series datasets — including daily excess returns across 89 markets and over 2 billion observations — to ensure full temporal and domain alignment.Bias-Free Design:
Training strictly follows a chronological expanding-window setup, avoiding any look-ahead bias or information leakage.Model Families:
This release includes variants of Chronos and TimesFM architectures adapted for financial time series:- Chronos-Tiny / Mini / Small
- TimesFM-8M / 20M
Performance Insights:
Our findings show that off-the-shelf TSFMs underperform in zero-shot forecasting, while finance-pretrained models achieve large gains in both predictive accuracy and portfolio Sharpe ratios.Evaluation Scope:
Models are benchmarked across U.S. and international markets, using rolling windows (5, 21, 252, 512 days) and 18M+ out-of-sample forecasts.
🧠 Technical Overview
- Architecture: Transformer-based TSFMs (Chronos & TimesFM)
- Training Regime: Pre-training from scratch, fine-tuning, and zero-shot evaluation
- Compute: >50,000 GPU hours on NVIDIA GH200 Grace Hopper clusters
📚 Citation
Please cite the accompanying paper if you use these models:
Re(Visiting) Time Series Foundation Models in Finance.
Rahimikia, Eghbal; Ni, Hao; Wang, Weiguan.
SSRN: https://ssrn.com/abstract=4963618
DOI: 10.2139/ssrn.4963618
🔋 Acknowledgments
This project was made possible through computational and institutional support from:
- Isambard-AI National AI Research Resource (AIRR)
- The University of Manchester (Research IT & Computational Shared Facility)
- Alliance Manchester Business School (AMBS), University of Manchester
- University College London (UCL)
- Shanghai University
- N8 Centre of Excellence in Computationally Intensive Research (N8 CIR)
- The Alan Turing Institute
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