kronos-predictor-finetuned

Kronos Predictor - Fine-tuned on Custom Dataset

This is a fine-tuned version of Kronos predictor, adapted for better performance on custom financial datasets.

Model Details

• Model Type: Predictor
• Base Model: NeoQuasar/Kronos-small
• Fine-tuned For: Financial Time Series Prediction
• Architecture: Transformer-based with custom tokenization
• Input: OHLCV (Open, High, Low, Close, Volume, Amount) data
• Output: Multi-step time series predictions

Training Details

• Training Data: Crypto Dataset (BTC, ETH, SOL, XAU)
• Time Range: 2022-01-21 to 2025-09-16
• Frequency: 5-minute intervals
• Sequence Length: 90 historical points
• Prediction Horizon: 10 future points

Usage

For Tokenizer

from model.kronos import KronosTokenizer

tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-small")
# Your tokenization code here

For Predictor

from model.kronos import Kronos, KronosTokenizer, KronosPredictor

tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-small")
model = Kronos.from_pretrained("NeoQuasar/Kronos-small")
predictor = KronosPredictor(model, tokenizer, device="cuda")

# Your prediction code here
predictions = predictor.predict(...)

With the Original Repository

# Clone the Kronos repository
git clone https://github.com/shiyu-coder/Kronos.git
cd Kronos

# Use the fine-tuned models
python examples/use_finetuned_model.py \
    --csv_data your_data.csv \
    --lookback 400 \
    --pred_len 120

Performance

This fine-tuned model shows improved performance on the target domain compared to the base model:

• Domain Adaptation: Specialized for the training dataset characteristics
• Numerical Stability: Improved convergence during fine-tuning
• Inference Speed: Optimized for the target sequence lengths

Limitations

• Optimized for 5-minute financial data intervals
• May require re-tuning for different time frequencies
• Performance may vary on datasets with different statistical properties

Citation

@misc{shi2025kronos,
      title={Kronos: A Foundation Model for the Language of Financial Markets},
      author={Yu Shi and Zongliang Fu and Shuo Chen and Bohan Zhao and Wei Xu and Changshui Zhang and Jian Li},
      year={2025},
      eprint={2508.02739},
      archivePrefix={arXiv},
      primaryClass={q-fin.ST},
      url={https://arxiv.org/abs/2508.02739},
}

Contact

For questions or issues, please open an issue on the Kronos GitHub repository.