JUSTiiNSANE/granite-timeseries-ttm-r2-gguf

Granite Time Series TTM-R2 (GGUF)

Summary

This repository hosts the Granite Time Series TinyTimeMixer (TTM-R2) model in lightweight, portable formats (GGUF, TorchScript, ONNX, MLX).
The original IBM Granite model is a compact forecasting network designed to handle common time-series patterns: cyclical, random, seasonal, and trend.

The purpose of this repo is to make the model practical for local use on consumer hardware such as Apple Silicon laptops. Instead of needing large GPUs or servers, you can test, benchmark, and run forecasting workflows directly on a MacBook or similar device.

What’s inside

• Model formats provided

  • FP16 GGUF
  • Quantized GGUF (q6_k)
  • TorchScript export
  • ONNX export
  • MLX (Apple Metal-accelerated format)

• Usage examples

  • Forecasting on synthetic patterns (cyclical, seasonal, trend).
  • Quick evaluation with provided scripts.
  • Conversion scripts showing how to move between formats.

• Why this matters

  • Lets you run state-of-the-art time-series forecasting with minimal hardware.
  • Useful for research, prototyping, or experimentation in finance, IoT, or general prediction tasks.
  • Provides a base for extending to more complex local forecasting setups.

• Audience
  Developers and researchers who want small, fast models for forecasting without depending on large GPU clusters.

IBM Granite Time Series Models

Model Card for Granite Time Series Models

Model Details

• Model Developers: IBM Research
• Architecture: Tiny Time Mixer (TTM)
• Input/Output: Multivariate time series → Future value predictions
• Granite TTM Variants: TTM-R1, TTM-R2 (this repository uses R2)

Model Description

Granite Time Series models are purpose-built to forecast time series signals. They are designed to capture a wide range of temporal patterns, including:

• Cyclical patterns
• Random fluctuations
• Seasonal effects
• Long-term trends

TTM models are lightweight and efficient, making them well-suited for resource-constrained environments and edge devices.

Intended Use

• Time-series forecasting for business, industrial, or research use cases.
• Evaluation and benchmarking on standard datasets.
• Academic exploration of compact time-series architectures.

Limitations

• Designed for univariate and multivariate series with typical forecasting horizons.
• Not optimized for extremely long sequences or tasks outside forecasting.

Training

• Trained on a mixture of synthetic and real-world datasets.
• Objective: minimize forecasting error across diverse temporal patterns.

Evaluation

Granite TTM models are benchmarked on common forecasting tasks and compared against classical baselines and other neural architectures. Performance is competitive while maintaining a small footprint.

Ethical Considerations

• Ensure proper use in domains where forecasting has real-world consequences (e.g., finance, healthcare).
• Misuse of forecasts can lead to poor decision-making; human oversight is required.

Citation

If you use Granite Time Series models in your research, please cite the original IBM Research paper and model card.

See https://huggingface.co/ibm-granite/granite-timeseries-ttm-r2

@inproceedings{ekambaram2024tinytimemixersttms,
      title={Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series},
      author={Vijay Ekambaram and Arindam Jati and Pankaj Dayama and Sumanta Mukherjee and Nam H. Nguyen and Wesley M. Gifford and Chandra Reddy and Jayant Kalagnanam},
      booktitle={Advances in Neural Information Processing Systems (NeurIPS 2024)},
      year={2024},
}