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Spatio-temporal Time Series Forecasting on PEMS08 (test)

9.07MAPEGWN*
8.860410.275211.6913.1048Nov 7, 2023Apr 12, 2024Sep 17, 2024Feb 22, 2025Jul 30, 2025Jan 4, 2026Jun 11, 2026

Evaluation results

MethodLinks
GWN*pre-training=STEP2023.119.0714.0823.33
GWNet + MP3Backbone=GWNet, Enhancement=MP32026.069.1413.7721.43
GWN*pre-training=GPT-ST2023.119.1613.9622.83
GWNet + GPT-STBackbone=GWNet, Enhancement=GPT-ST2026.069.1613.9622.83
GWNetBackbone=GWNet, Enhancement=None2026.069.2114.423.39
GWN*pre-training=none2023.119.2813.9722.94
GWNpre-training=GPT-ST2023.119.4214.7224.12
MTGNNpre-training=GPT-ST2023.119.6714.9324.46
MTGNNpre-training=none2023.119.7215.2824.7
STWApre-training=GPT-ST2023.119.8115.2924.5
STWA + GPT-STBackbone=STWA, Enhancement=GPT-ST2026.069.8115.2924.5
STNorm + MP3Backbone=STNorm, Enhancement=MP32026.069.8514.7322.4
STGCN + MP3Backbone=STGCN, Enhancement=MP32026.069.8714.8322.81
GWNpre-training=none2023.119.9815.2924.59
STNorm + GPT-STBackbone=STNorm, Enhancement=GPT-ST2026.0610.0315.125.1
STNormBackbone=STNorm, Enhancement=None2026.0610.0515.8223.74
STWA + MP3Backbone=STWA, Enhancement=MP32026.0610.1915.0322.71
ASTGCNpre-training=GPT-ST2023.1110.2815.8924.87
MSDR + MP3Backbone=MSDR, Enhancement=MP32026.0610.2815.1823.32
MSDRpre-training=GPT-ST2023.1110.3315.9225.03
MSDR + GPT-STBackbone=MSDR, Enhancement=GPT-ST2026.0610.3315.9225.03
STGCNpre-training=GPT-ST2023.1110.4516.2425.93
STGCN + GPT-STBackbone=STGCN, Enhancement=GPT-ST2026.0610.4516.2425.93
STGODEpre-training=GPT-ST2023.1110.4615.6824.84
STFGNNpre-training=GPT-ST2023.1110.5315.8925.78
MSDRpre-training=none2023.1110.5416.4725.63
STWApre-training=none2023.1110.5516.1625.8
STWABackbone=STWA, Enhancement=None2026.0610.5516.1625.8
STSGCNpre-training=GPT-ST2023.1110.5916.2926.14
MSDRBackbone=MSDR, Enhancement=None2026.0610.616.3324.69
Whole DatasetRemaining Ratio %=100%, Pruning Category=None, Backbone=ST backbone2026.0210.98——
STGCNBackbone=STGCN, Enhancement=None2026.0611.0716.2525.13
STGCNpre-training=none2023.1111.0817.8528.64
STFGNNpre-training=none2023.1111.117.1327.32
ST-PruneRemaining Ratio %=50%, Pruning Category=Dynamic, Backbone=ST backbone2026.0211.11——
ST-PruneRemaining Ratio %=70%, Pruning Category=Dynamic, Backbone=ST backbone2026.0211.19——
e-greedyRemaining Ratio %=70%, Pruning Category=Dynamic, Backbone=ST backbone2026.0211.24——
GraphCutRemaining Ratio %=70%, Pruning Category=Static, Backbone=ST backbone2026.0211.27——
EntropyRemaining Ratio %=70%, Pruning Category=Static, Backbone=ST backbone2026.0211.28——
UCBRemaining Ratio %=70%, Pruning Category=Dynamic, Backbone=ST backbone2026.0211.28——
ForgettingRemaining Ratio %=70%, Pruning Category=Static, Backbone=ST backbone2026.0211.29——
InfoBatchRemaining Ratio %=70%, Pruning Category=Dynamic, Backbone=ST backbone2026.0211.33——
Soft RandomRemaining Ratio %=70%, Pruning Category=Dynamic, Backbone=ST backbone2026.0211.34——
MarginRemaining Ratio %=70%, Pruning Category=Static, Backbone=ST backbone2026.0211.38——
STGODEpre-training=none2023.1111.3917.7327.53
FaLoRemaining Ratio %=70%, Pruning Category=Static, Backbone=ST backbone2026.0211.4——
ForgettingRemaining Ratio %=50%, Pruning Category=Static, Backbone=ST backbone2026.0211.46——
STSGCNpre-training=none2023.1111.4917.8227.88
GraNdRemaining Ratio %=70%, Pruning Category=Static, Backbone=ST backbone2026.0211.5——
K-MeansRemaining Ratio %=70%, Pruning Category=Static, Backbone=ST backbone2026.0211.52——
HerdingRemaining Ratio %=50%, Pruning Category=Static, Backbone=ST backbone2026.0211.58——
MarginRemaining Ratio %=50%, Pruning Category=Static, Backbone=ST backbone2026.0211.6——
Least ConfidenceRemaining Ratio %=70%, Pruning Category=Static, Backbone=ST backbone2026.0211.61——
Least ConfidenceRemaining Ratio %=50%, Pruning Category=Static, Backbone=ST backbone2026.0211.74——
GlisterRemaining Ratio %=70%, Pruning Category=Static, Backbone=ST backbone2026.0211.74——
ST-PruneRemaining Ratio %=30%, Pruning Category=Dynamic, Backbone=ST backbone2026.0211.75——
UCBRemaining Ratio %=50%, Pruning Category=Dynamic, Backbone=ST backbone2026.0211.76——
ASTGCNpre-training=none2023.1111.7817.5526.97
CDRemaining Ratio %=70%, Pruning Category=Static, Backbone=ST backbone2026.0211.83——
InfoBatchRemaining Ratio %=50%, Pruning Category=Dynamic, Backbone=ST backbone2026.0211.83——
GraphCutRemaining Ratio %=50%, Pruning Category=Static, Backbone=ST backbone2026.0211.84——
e-greedyRemaining Ratio %=50%, Pruning Category=Dynamic, Backbone=ST backbone2026.0211.84——
CDRemaining Ratio %=50%, Pruning Category=Static, Backbone=ST backbone2026.0211.96——
Soft RandomRemaining Ratio %=50%, Pruning Category=Dynamic, Backbone=ST backbone2026.0212.01——
Hard RandomRemaining Ratio %=70%, Pruning Category=Static, Backbone=ST backbone2026.0212.06——
Hard RandomRemaining Ratio %=50%, Pruning Category=Static, Backbone=ST backbone2026.0212.07——
HerdingRemaining Ratio %=30%, Pruning Category=Static, Backbone=ST backbone2026.0212.11——
InfoBatchRemaining Ratio %=30%, Pruning Category=Dynamic, Backbone=ST backbone2026.0212.12——
FaLoRemaining Ratio %=50%, Pruning Category=Static, Backbone=ST backbone2026.0212.13——
ST-PruneRemaining Ratio %=10%, Pruning Category=Dynamic, Backbone=ST backbone2026.0212.13——
ForgettingRemaining Ratio %=30%, Pruning Category=Static, Backbone=ST backbone2026.0212.19——
e-greedyRemaining Ratio %=30%, Pruning Category=Dynamic, Backbone=ST backbone2026.0212.21——
K-MeansRemaining Ratio %=30%, Pruning Category=Static, Backbone=ST backbone2026.0212.24——
CDRemaining Ratio %=30%, Pruning Category=Static, Backbone=ST backbone2026.0212.25——
TGCNpre-training=GPT-ST2023.1112.2617.3426.53
GraphCutRemaining Ratio %=30%, Pruning Category=Static, Backbone=ST backbone2026.0212.26——
GraNdRemaining Ratio %=50%, Pruning Category=Static, Backbone=ST backbone2026.0212.33——
CalRemaining Ratio %=30%, Pruning Category=Static, Backbone=ST backbone2026.0212.4——
CalRemaining Ratio %=70%, Pruning Category=Static, Backbone=ST backbone2026.0212.4——
Soft RandomRemaining Ratio %=30%, Pruning Category=Dynamic, Backbone=ST backbone2026.0212.41——
UCBRemaining Ratio %=30%, Pruning Category=Dynamic, Backbone=ST backbone2026.0212.41——
K-MeansRemaining Ratio %=50%, Pruning Category=Static, Backbone=ST backbone2026.0212.48——
GlisterRemaining Ratio %=50%, Pruning Category=Static, Backbone=ST backbone2026.0212.51——
MarginRemaining Ratio %=30%, Pruning Category=Static, Backbone=ST backbone2026.0212.55——
FaLoRemaining Ratio %=30%, Pruning Category=Static, Backbone=ST backbone2026.0212.56——
Least ConfidenceRemaining Ratio %=30%, Pruning Category=Static, Backbone=ST backbone2026.0212.62——
CalRemaining Ratio %=50%, Pruning Category=Static, Backbone=ST backbone2026.0212.64——
EntropyRemaining Ratio %=30%, Pruning Category=Static, Backbone=ST backbone2026.0212.82——
EntropyRemaining Ratio %=50%, Pruning Category=Static, Backbone=ST backbone2026.0212.83——
HerdingRemaining Ratio %=70%, Pruning Category=Static, Backbone=ST backbone2026.0212.84——
GraNdRemaining Ratio %=30%, Pruning Category=Static, Backbone=ST backbone2026.0213.09——
EntropyRemaining Ratio %=10%, Pruning Category=Static, Backbone=ST backbone2026.0213.22——
GlisterRemaining Ratio %=30%, Pruning Category=Static, Backbone=ST backbone2026.0213.23——
InfoBatchRemaining Ratio %=10%, Pruning Category=Dynamic, Backbone=ST backbone2026.0213.66——
UCBRemaining Ratio %=10%, Pruning Category=Dynamic, Backbone=ST backbone2026.0213.8——
e-greedyRemaining Ratio %=10%, Pruning Category=Dynamic, Backbone=ST backbone2026.0213.82——
Hard RandomRemaining Ratio %=10%, Pruning Category=Static, Backbone=ST backbone2026.0213.93——
GraNdRemaining Ratio %=10%, Pruning Category=Static, Backbone=ST backbone2026.0213.93——
MarginRemaining Ratio %=10%, Pruning Category=Static, Backbone=ST backbone2026.0214.06——
Least ConfidenceRemaining Ratio %=10%, Pruning Category=Static, Backbone=ST backbone2026.0214.31——