Lorenz
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Causal Discovery | Lorenz-96 | AUROC100 | 36 | |
| Filtering | Lorenz–63 | Average EW25.744 | 18 | |
| Filtering | Lorenz-96 10-dimensional | RMSE0.592 | 18 | |
| Time Series Forecasting | Lorenz Base | MSE1.34 | 17 | |
| Probabilistic Time Series Forecasting | Lorenz-Base H=192 | CRPS0.823 | 10 | |
| Probabilistic Time Series Forecasting | Lorenz-Base H=64 | CRPS0.295 | 10 | |
| Causal Pruning | Lorenz F=40 T=500 | PR62.2 | 9 | |
| Causal Pruning | Lorenz F=40 T=250 | PR Score71.2 | 9 | |
| Causal Pruning | Lorenz F=10 T=500 | PR60.2 | 9 | |
| Causal Pruning | Lorenz F=10 (T=250) | PR61.17 | 9 | |
| Data Assimilation | Lorenz 1963 | RMSE0.0502 | 8 | |
| Segment-level regime discovery | Lorenz | ARI1 | 8 | |
| Equation Discovery | Lorenz 96 Noise Levels: 0%, 1%, 10% (large) ODE | Mp1 | 8 | |
| Data Assimilation | Lorenz 1963 (eval) | RMSE0.0502 | 6 | |
| Chaotic system forecasting | Lorenz-63 | VPT (0% noise)1.2 | 6 | |
| Chaotic system prediction | Lorenz (test) | RMSE (128 steps)0.065 | 5 | |
| Time-series forecasting | Lorenz | Coverage@90%91 | 5 | |
| Time-series imputation | Lorenz 3D (ID) | RMSE4.2178 | 4 | |
| Time Series Forecasting | Lorenz | MSE21.82 | 4 | |
| Conservation-law discovery | lorenz (test) | F1 Score0 | 4 | |
| Vector Quantization | Lorenz | Act (%)99 | 4 | |
| Synthetic Dynamical System Modeling | Lorenz | Dynamics MSE0.141 | 4 | |
| State reconstruction | Lorenz 3D (Out-of-Distribution) | RMSE1.1551 | 3 | |
| State reconstruction | Lorenz 3D (In-Distribution) | RMSE2.2509 | 3 | |
| Dynamics Modeling | Lorenz LO 1 single densely observed trajectory | Log Likelihood-0.57 | 3 |
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