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Epidemic Forecasting on China TB medium-term (test)

12.3SMAPEGSTAR
7.90237.588567.27596.9615Mar 7, 2026

Evaluation results

MethodLinks
GSTARModel Type=Spatiotemporal Models2026.0312.3320.86951.751.361.07139.57129.130.140.12320.86—
NHiTSModel Type=Temporal Models2026.0312.91328.93440.731.211.05151.57145.130.150.13328.93—
LSTMModel Type=Temporal Models2026.0314.96350.56728.071.490.82168.19164.640.160.15350.56—
TransformersModel Type=Temporal Models2026.0315.29368.59450.991.421.22161.44150.020.170.14368.59—
STESNModel Type=ST-Engression Models2026.0315.35363.11666.151.540.75158.26135.320.160.13343.411,485.91
MVENModel Type=ST-Engression Models2026.0315.51359.42744.891.530.81157.36112.320.150.12304.967,294.79
GCENModel Type=ST-Engression Models2026.0316.72415.46932.841.761.05167.72123.150.190.13361.449,992.48
STENModel Type=ST-Engression Models2026.0317.04404.03812.761.720.91162.49112.830.180.12347.778,795.67
DeepARModel Type=Probabilistic Models2026.0317.11405.15718.911.720.81177.3199.450.180.13328.875,154.01
STGCNModel Type=Spatiotemporal Models2026.0319.39486.081,226.492.061.38206.25187.850.220.18486.08—
STARMAModel Type=Spatiotemporal Models2026.0329.03601.67963.322.561.08437.17505.340.270.44601.67—
TCNModel Type=Temporal Models2026.0331.01544.52625.192.191.74351.68391.380.250.34544.52—
GpGpModel Type=Probabilistic Models2026.0364.061,269.761,705.625.391.92517.96220.750.570.32955.189,957.39
DiffSTGModel Type=Probabilistic Models2026.0365.281,370.151,728.15.821.94622.62256.890.620.381,062.1211,358.48
Prob-iTransformerModel Type=Probabilistic Models2026.03122.251,884.42,470.4882.781,339.221,301.160.851.241,704.9448,839.51