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ResearchFormerly WizWand

Time Series Classification on Handwriting

64AccuracySimMTM
0.35216.87633.449.924Feb 13, 2022Nov 1, 2022Jul 21, 2023Apr 8, 2024Dec 25, 2024Sep 13, 2025Jun 2, 2026
Updated 5mo ago

Evaluation results

MethodLinks
SimMTM2023.1164
RocketMethod Category=Classical Methods2022.0258.8
TS-TCC2023.1155
Unsupervised TCNMethod Category=TCN2022.0253.3
TS2Vec2023.1152
CRT2023.1152
LPTM2023.1151
LongformerMethod Category=Transformer2022.0239.6
TST2023.1137
Autoformer2026.0336.7
VI 2D Mamba2026.0335.3
GTM2025.0234.8
Linear TransformerMethod Category=Transformer2022.0234.7
cosFormerMethod Category=Transformer2022.0234.7
FlowformerMethod Category=Transformer2022.0233.8
Flowformer2026.0333.8
LETBackbone=Qwen-0.5B, Alignment depth=6, Teacher Model=TimesNet2026.0233.2
Chimera2026.0332.9
Informer2026.0332.8
GPT4TS2025.0232.7
ETSformer2026.0332.5
PerformerMethod Category=Transformer2022.0232.1
TimesNet2026.0332.1
TimesNet2025.0232.1
TransformerMethod Category=Transformer2022.0232
Stationary2026.0331.6
YOSO-EMethod Category=Transformer2022.0230.9
MTCN2026.0330.6
PatchTST2026.0329.6
Pyraformer2026.0329.4
TimesNet2023.1129
SoftMethod Category=Transformer2022.0228.9
DTWMethod Category=Classical Methods2022.0228.6
FEDformer2026.0328
ReformerMethod Category=Transformer2022.0227.4
Reformer2026.0327.4
AarenArchitecture=Aaren2024.0527.39
DLinear2026.0327
TransformerArchitecture=Transformer2024.0526.54
LightTS2026.0326.1
LSTNet2026.0325.8
LSSL2026.0324.6
iTransformer2025.0224.2
TARNet2023.1124
Qwen-0.5BBackbone=Qwen-0.5B, Training=fine-tuning2026.0223
DataOobModel=Nonstationary Transformer2025.0622.4
TSRatingModel=Nonstationary Transformer2025.0621.3
TSRatingModel=Informer2025.0620.9
RandomModel=Nonstationary Transformer2025.0620.7
TimeInfModel=Nonstationary Transformer2025.0620.5
DataOobModel=Informer2025.0619.6
TimeInfModel=Informer2025.0619.5
RandomModel=Informer2025.0619.3
KNNShapleyModel=Nonstationary Transformer2025.0618.8
DataShapleyModel=Informer2025.0618.7
KNNShapleyModel=Informer2025.0618.6
Autoformer2023.1118
DataShapleyModel=Nonstationary Transformer2025.0617.9
TSQAgentModel=PatchTST, Data Budget=50%2026.0616.6
TSQAgentModel=CNN, Data Budget=50%2026.0616.2
Informer2023.1116
DataShapleyModel=CNN, Data Budget=50%2026.0615.9
TSRatingModel=CNN, Data Budget=50%2026.0615.9
XGBoostMethod Category=Classical Methods2022.0215.8
TSRatingModel=PatchTST, Data Budget=50%2026.0615.6
DataOobModel=CNN, Data Budget=50%2026.0615.5
TimeInfModel=CNN, Data Budget=50%2026.0615.5
LSTMMethod Category=RNN2022.0215.2
LSTM2026.0315.2
RandomModel=CNN, Data Budget=50%2026.0615.1
DataOobModel=PatchTST, Data Budget=50%2026.0614.1
TimeInfModel=PatchTST, Data Budget=50%2026.0613.1
DataShapleyModel=PatchTST, Data Budget=50%2026.0612.6
RandomModel=PatchTST, Data Budget=50%2026.0612.4
TSQAgentModel=Linear, Data Budget=50%2026.065.6
RandomModel=Linear, Data Budget=50%2026.065.3
TSRatingModel=Linear, Data Budget=50%2026.064.9
TimeInfModel=Linear, Data Budget=50%2026.064.2
DataShapleyModel=Linear, Data Budget=50%2026.063.8
DataOobModel=Linear, Data Budget=50%2026.062.8