Energy
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Time-series Generation | Energy | Discriminative Score0.553 | 99 | |
| Time Series Forecasting | Energy | MSE0.065 | 72 | |
| Data Imputation | energy MNAR | Wasserstein Distance0.3714 | 65 | |
| Data Imputation | energy MAR | Wasserstein Distance0.3002 | 65 | |
| Data Imputation | energy MCAR | Wasserstein distance0.3024 | 65 | |
| Tabular Data Synthesis Fidelity | energy | KS Distance0.62 | 42 | |
| Regression | Energy UCI (test) | RMSE0.04 | 37 | |
| Time-series Generation | Energy (Evening Peak) | Context-FID0.0315 | 27 | |
| Regression | Energy | RMSE0.112 | 24 | |
| Linear Regression | energy (val) | MSE7.2562 | 22 | |
| Tabular Data Synthesis | energy | Inverse KL0.12 | 21 | |
| Tabular Data Synthesis | energy | Chi-squared Test Result0.03 | 21 | |
| Anomaly Detection | Energy | A-R (AUC-ROC)81.26 | 20 | |
| Anomaly Detection | Energy | Accuracy80 | 20 | |
| Classification | energy | Balanced Accuracy35.74 | 20 | |
| Time-series Generation | Energy (Morning Peak) | Context-FID0.0323 | 18 | |
| Anomaly Detection | Energy | Aff-F Score74.37 | 18 | |
| Prediction Region Estimation | Energy (test) | Coverage91.1 | 16 | |
| Regression | Energy 100% non-corrupted features | RMSE0.08 | 15 | |
| Regression | Energy 50% non-corrupted features | RMSE0.082 | 15 | |
| Regression | Energy 0% non-corrupted features | RMSE0.089 | 15 | |
| Regression | Energy (UCI) | Log-Likelihood-0.539 | 13 | |
| Regression | Energy Heating (test) | Test R^21 | 12 | |
| Regression | Energy Cooling (test) | Test R^2210 | 12 | |
| Tabular Regression | Energy UCI 10% outlier contamination | RMSE1.89 | 11 |
Showing 25 of 88 rows