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---
license: cc-by-4.0
pipeline_tag: time-series-forecasting
datasets:
- williamgilpin/dysts
---
# DynaMix
[](https://arxiv.org/abs/2505.13192) (accepted NeurIPS 2025 paper)
DynaMix is a foundation model for zero-shot inference of dynamical systems that preserves long-term statistics. Unlike traditional approaches that require retraining for each new system, DynaMix provides context driven generalization to unseen dynamical systems.
- **Accurate Zero-Shot Dynamical Systems Reconstruction**: DynaMix generalizes across diverse dynamical systems without fine-tuning, accurately capturing attractor geometry and long-term statistics.
- **Context Felxible Dynamics Modeling**: The multivariate architecture captures dependencies across system dimensions and adapts to different dimensionalities and context lengths.
- **Efficient and Lightweight**: Designed to be efficient with a few thousand parameters, DynaMix can also run on CPU for inference, and is enabling orders-of-magnitude faster inference than traditional foundation models.
- **General Time Series Forecasting**: Extends beyond DSR to general time series forecasting using adaptable embedding techniques.
For complete documentation and code, visit the [DynaMix repository](https://github.com/DurstewitzLab/DynaMix-python).
## Model Description
DynaMix is based on a mixture of experts (MoE) architecture operating in latent space:
1. **Expert Networks**: Each expert is a specialized dynamical model, given trhough RNN based architectures
2. **Gating Network**: Selects experts based on the provided context and current latent representation of the dynamics
By aggregating the expert weighting with the expert prediction the next state is predicted.
## Usage
To load the model in python using the corresponding codebase [DynaMix repository](https://github.com/DurstewitzLab/DynaMix-python), use:
```python
from src.utilities.utilities import load_hf_model
# Initialize model with architecture
model = load_hf_model(model_name="dynamix-3d-alrnn-v1.0")
```
Given context data from the target system with shape (`T_C`, `S`, `N`) (where `T_C` is the context length, `S` the number of sequences that should get processed and `N` the data dimensionality), generate forecasts by passing the data through the `DynaMixForecaster` along with the loaded model. Further details can be found in the GitHub repository [DynaMix repository](https://github.com/yourusername/zero-shot-DSR).
## Citation
If you use DynaMix in your research, please cite our paper:
```
@misc{hemmer2025truezeroshotinferencedynamical,
title={True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics},
author={Christoph Jürgen Hemmer and Daniel Durstewitz},
year={2025},
eprint={2505.13192},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.13192},
}
``` |