| --- |
| license: cc-by-nc-4.0 |
| library_name: pathfm |
| pipeline_tag: time-series-forecasting |
| tags: |
| - time-series |
| - forecasting |
| - multivariate |
| - flow-matching |
| --- |
| |
| # PathFM-300M |
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| PathFM makes trajectory forecasting fast and practical. |
| It generates multivariate futures in one step with competitive marginal accuracy and leading trajectory quality. |
| Get sample paths, quantiles, and other higher-order statistics through one model. |
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| Our model also demonstrates strong performance in short- and long-horizon chaotic systems. |
| Chaotic models: [75M](https://huggingface.co/autogluon/pathfm-chaotic-75m) and [125M](https://huggingface.co/autogluon/pathfm-chaotic-125m). |
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| ## Model Details |
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| - **Layers:** 30 transformer layers. |
| - **Hidden Dimension:** 768. |
| - **Attention Heads:** 12 (64 dimensions per head). |
| - **Feed-forward Dimension:** 3,072. |
| - **Patch Length:** 16 time steps. |
| - **Context Length:** 512 observed steps. |
| - **Forecast Horizon:** up to 480 steps with one model call. |
| - **Default Sampler:** one-step parallel decoding. |
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| ## Performance |
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| | Model | FEV skill ↑ | FPS skill ↑ | TIME MASE ↓ | TIME CRPS ↓ | GIFT MASE ↓ | GIFT CRPS ↓ | |
| |---|---:|---:|---:|---:|---:|---:| |
| | [PathFM-125M](https://huggingface.co/autogluon/pathfm-125m) | 0.4640 | 0.5386 | 0.6560 | 0.5531 | 0.7073 | 0.4878 | |
| | [PathFM-300M](https://huggingface.co/autogluon/pathfm-300m) | 0.4735 | 0.5458 | 0.6538 | 0.5523 | 0.7008 | 0.4818 | |
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| FPS combines six complementary component skills to evaluate complete forecast trajectories on general corpus, capturing temporal dependencies rather than only the marginal distribution at each forecast step. |
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| ## Code |
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| For installation, inference examples, and benchmark evaluation, see [autogluon/PathFM](https://github.com/autogluon/PathFM). |
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