metadata
license: cc-by-nc-4.0
library_name: pathfm
pipeline_tag: time-series-forecasting
tags:
- time-series
- forecasting
- multivariate
- flow-matching
PathFM-300M
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.
Our model also demonstrates strong performance in short- and long-horizon chaotic systems. Chaotic models: 75M and 125M.
Model Details
- 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.
Performance
| Model | FEV skill ↑ | FPS skill ↑ | TIME MASE ↓ | TIME CRPS ↓ | GIFT MASE ↓ | GIFT CRPS ↓ |
|---|---|---|---|---|---|---|
| PathFM-125M | 0.4640 | 0.5386 | 0.6560 | 0.5531 | 0.7073 | 0.4878 |
| PathFM-300M | 0.4735 | 0.5458 | 0.6538 | 0.5523 | 0.7008 | 0.4818 |
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.
Code
For installation, inference examples, and benchmark evaluation, see autogluon/PathFM.