| | --- |
| | 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/DurstewitzLab/DynaMix-python). |
| |
|
| |
|
| | ## 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}, |
| | } |
| | ``` |
