Time Series Classification on PEMS-SF
98.3AccuracyXGBoost
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| XGBoostMethod Category=Classical Methods2022.02 | 98.3 | — | — | |
| TARNet2023.11 | 94 | — | — | |
| TST2023.11 | 93 | — | — | |
| LPTM2023.11 | 93 | — | — | |
| TimesNet2024.11 | 89.6 | — | — | |
| TimesNet2026.03 | 89.6 | — | — | |
| TimesNet2025.02 | 89.6 | — | — | |
| Chimera2026.03 | 89.5 | — | — | |
| PatchTST2026.03 | 89.3 | — | — | |
| MTCN2026.03 | 89.1 | — | — | |
| CRT2023.11 | 89 | — | — | |
| LightTS2024.11 | 88.4 | — | — | |
| LightTS2026.03 | 88.4 | — | — | |
| GTM2025.02 | 88.4 | — | — | |
| Peri-midFormer2024.11 | 88.2 | — | — | |
| VI 2D Mamba2026.03 | 88.1 | — | — | |
| GPT4TS2024.11 | 87.9 | — | — | |
| GPT4TS2025.02 | 87.9 | — | — | |
| iTransformer2025.02 | 87.9 | — | — | |
| Non-stationary Transformer2024.11 | 87.3 | — | — | |
| Stationary2026.03 | 87.3 | — | — | |
| LSTNet2026.03 | 86.7 | — | — | |
| LSSL2026.03 | 86.1 | — | — | |
| ETSformer2024.11 | 86 | — | — | |
| SimMTM2023.11 | 86 | — | — | |
| ETSformer2026.03 | 86 | — | — | |
| YOSO-EMethod Category=Transformer2022.02 | 85.2 | — | — | |
| TimesNet2023.11 | 84 | — | — | |
| LongformerMethod Category=Transformer2022.02 | 83.8 | — | — | |
| FlowformerMethod Category=Transformer2022.02 | 83.8 | — | — | |
| Flowformer2024.11 | 83.8 | — | — | |
| TSLANet2024.11 | 83.8 | — | — | |
| Flowformer2026.03 | 83.8 | — | — | |
| SoftMethod Category=Transformer2022.02 | 83.2 | — | — | |
| Pyraformer2024.11 | 83.2 | — | — | |
| Pyraformer2026.03 | 83.2 | — | — | |
| UNITS-SUP2025.02 | 83.2 | — | — | |
| ReformerMethod Category=Transformer2022.02 | 82.7 | — | — | |
| Reformer2024.11 | 82.7 | — | — | |
| Autoformer2024.11 | 82.7 | — | — | |
| Reformer2026.03 | 82.7 | — | — | |
| Autoformer2026.03 | 82.7 | — | — | |
| UNITS-PMT2025.02 | 82.7 | — | — | |
| TransformerMethod Category=Transformer2022.02 | 82.1 | — | — | |
| Linear TransformerMethod Category=Transformer2022.02 | 82.1 | — | — | |
| Transformer2024.11 | 82.1 | — | — | |
| AarenArchitecture=Aaren2024.05 | 81.85 | — | — | |
| Informer2026.03 | 81.5 | — | — | |
| PerformerMethod Category=Transformer2022.02 | 80.9 | — | — | |
| cosFormerMethod Category=Transformer2022.02 | 80.9 | — | — | |
| PatchTST2024.11 | 80.9 | — | — | |
| FEDformer2024.11 | 80.9 | — | — | |
| FEDformer2026.03 | 80.9 | — | — | |
| TransformerArchitecture=Transformer2024.05 | 78.73 | — | — | |
| RocketMethod Category=Classical Methods2022.02 | 75.1 | — | — | |
| Rocket2024.11 | 75.1 | — | — | |
| DLinear2024.11 | 75.1 | — | — | |
| DLinear2026.03 | 75.1 | — | — | |
| TS2Vec2023.11 | 75 | — | — | |
| TS-TCC2023.11 | 73 | — | — | |
| DTWMethod Category=Classical Methods2022.02 | 71.1 | — | — | |
| Autoformer2023.11 | 71 | — | — | |
| Unsupervised TCNMethod Category=TCN2022.02 | 68.8 | — | — | |
| TCN2024.11 | 68.8 | — | — | |
| Informer2023.11 | 67 | — | — | |
| LETBackbone=Qwen-0.5B, Alignment depth=6, Teacher Model=TimesNet2026.02 | 62.5 | — | — | |
| Qwen-0.5BBackbone=Qwen-0.5B, Training=fine-tuning2026.02 | 45.5 | — | — | |
| LSTMMethod Category=RNN2022.02 | 39.9 | — | — | |
| LSTM2026.03 | 39.9 | — | — | |
| LETS-C2024.07 | — | 0.56 | 5.51 | |
| OneFitsAll2024.07 | — | 10.23 | — |