Time Series Classification on Handwriting
64AccuracySimMTM
Evaluation Results
| Method | Links | |
|---|---|---|
| SimMTM2023.11 | 64 | |
| RocketMethod Category=Classical Methods2022.02 | 58.8 | |
| TS-TCC2023.11 | 55 | |
| Unsupervised TCNMethod Category=TCN2022.02 | 53.3 | |
| TS2Vec2023.11 | 52 | |
| CRT2023.11 | 52 | |
| LPTM2023.11 | 51 | |
| LongformerMethod Category=Transformer2022.02 | 39.6 | |
| TST2023.11 | 37 | |
| Autoformer2026.03 | 36.7 | |
| VI 2D Mamba2026.03 | 35.3 | |
| GTM2025.02 | 34.8 | |
| Linear TransformerMethod Category=Transformer2022.02 | 34.7 | |
| cosFormerMethod Category=Transformer2022.02 | 34.7 | |
| FlowformerMethod Category=Transformer2022.02 | 33.8 | |
| Flowformer2026.03 | 33.8 | |
| LETBackbone=Qwen-0.5B, Alignment depth=6, Teacher Model=TimesNet2026.02 | 33.2 | |
| Chimera2026.03 | 32.9 | |
| Informer2026.03 | 32.8 | |
| GPT4TS2025.02 | 32.7 | |
| ETSformer2026.03 | 32.5 | |
| PerformerMethod Category=Transformer2022.02 | 32.1 | |
| TimesNet2026.03 | 32.1 | |
| TimesNet2025.02 | 32.1 | |
| TransformerMethod Category=Transformer2022.02 | 32 | |
| Stationary2026.03 | 31.6 | |
| YOSO-EMethod Category=Transformer2022.02 | 30.9 | |
| MTCN2026.03 | 30.6 | |
| PatchTST2026.03 | 29.6 | |
| Pyraformer2026.03 | 29.4 | |
| TimesNet2023.11 | 29 | |
| SoftMethod Category=Transformer2022.02 | 28.9 | |
| DTWMethod Category=Classical Methods2022.02 | 28.6 | |
| FEDformer2026.03 | 28 | |
| ReformerMethod Category=Transformer2022.02 | 27.4 | |
| Reformer2026.03 | 27.4 | |
| AarenArchitecture=Aaren2024.05 | 27.39 | |
| DLinear2026.03 | 27 | |
| TransformerArchitecture=Transformer2024.05 | 26.54 | |
| LightTS2026.03 | 26.1 | |
| LSTNet2026.03 | 25.8 | |
| LSSL2026.03 | 24.6 | |
| iTransformer2025.02 | 24.2 | |
| TARNet2023.11 | 24 | |
| Qwen-0.5BBackbone=Qwen-0.5B, Training=fine-tuning2026.02 | 23 | |
| DataOobModel=Nonstationary Transformer2025.06 | 22.4 | |
| TSRatingModel=Nonstationary Transformer2025.06 | 21.3 | |
| TSRatingModel=Informer2025.06 | 20.9 | |
| RandomModel=Nonstationary Transformer2025.06 | 20.7 | |
| TimeInfModel=Nonstationary Transformer2025.06 | 20.5 | |
| DataOobModel=Informer2025.06 | 19.6 | |
| TimeInfModel=Informer2025.06 | 19.5 | |
| RandomModel=Informer2025.06 | 19.3 | |
| KNNShapleyModel=Nonstationary Transformer2025.06 | 18.8 | |
| DataShapleyModel=Informer2025.06 | 18.7 | |
| KNNShapleyModel=Informer2025.06 | 18.6 | |
| Autoformer2023.11 | 18 | |
| DataShapleyModel=Nonstationary Transformer2025.06 | 17.9 | |
| TSQAgentModel=PatchTST, Data Budget=50%2026.06 | 16.6 | |
| TSQAgentModel=CNN, Data Budget=50%2026.06 | 16.2 | |
| Informer2023.11 | 16 | |
| DataShapleyModel=CNN, Data Budget=50%2026.06 | 15.9 | |
| TSRatingModel=CNN, Data Budget=50%2026.06 | 15.9 | |
| XGBoostMethod Category=Classical Methods2022.02 | 15.8 | |
| TSRatingModel=PatchTST, Data Budget=50%2026.06 | 15.6 | |
| DataOobModel=CNN, Data Budget=50%2026.06 | 15.5 | |
| TimeInfModel=CNN, Data Budget=50%2026.06 | 15.5 | |
| LSTMMethod Category=RNN2022.02 | 15.2 | |
| LSTM2026.03 | 15.2 | |
| RandomModel=CNN, Data Budget=50%2026.06 | 15.1 | |
| DataOobModel=PatchTST, Data Budget=50%2026.06 | 14.1 | |
| TimeInfModel=PatchTST, Data Budget=50%2026.06 | 13.1 | |
| DataShapleyModel=PatchTST, Data Budget=50%2026.06 | 12.6 | |
| RandomModel=PatchTST, Data Budget=50%2026.06 | 12.4 | |
| TSQAgentModel=Linear, Data Budget=50%2026.06 | 5.6 | |
| RandomModel=Linear, Data Budget=50%2026.06 | 5.3 | |
| TSRatingModel=Linear, Data Budget=50%2026.06 | 4.9 | |
| TimeInfModel=Linear, Data Budget=50%2026.06 | 4.2 | |
| DataShapleyModel=Linear, Data Budget=50%2026.06 | 3.8 | |
| DataOobModel=Linear, Data Budget=50%2026.06 | 2.8 |