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24-hour ahead probabilistic forecasting on NYISO 2018–2024

0.1281MSEUG-WIAE-GPF
-17.959176104.129937226.21905348.308163Mar 8, 2026

Evaluation results

MethodLinks
UG-WIAE-GPFModel Category=Proposed method (WIAE-based)2026.030.12810.2550.01190.0280.25150.022
WIAE-GPFYear=2024, Model Category=Proposed method (WIAE-based)2026.030.35080.38350.0220.03990.39580.399
GRUYear=2017, Model Category=Recurrent neural network baselines2026.030.41240.42450.00920.01261.10810.0644
PatchTSTYear=2022, Model Category=Transformer-/decomposition-based baselines2026.031.75081.08660.03870.0323.36370.1648
NBEATSYear=2019, Model Category=Transformer-/decomposition-based baselines2026.032.35780.95010.05210.0282.45690.1441
NHITSYear=2023, Model Category=Transformer-/decomposition-based baselines2026.032.78311.09880.06150.03242.91540.1666
RNNYear=2014, Model Category=Recurrent neural network baselines2026.033.69391.65080.08270.04884.640.2503
LSTMYear=2014, Model Category=Recurrent neural network baselines2026.034.23071.8180.09470.05385.11670.2757
DilatedRNNYear=2017, Model Category=Recurrent neural network baselines2026.036.19141.76390.13860.05224.84010.2675
TimesNetYear=2023, Model Category=Transformer-/decomposition-based baselines2026.036.38641.93250.1430.05715.4370.293
TimeMixerYear=2024, Model Category=Transformer-/decomposition-based baselines2026.039.15321.88710.20240.05564.91940.2862
MLPYear=2010, Model Category=Lightweight / linear / graph baselines2026.0314.36652.66690.31760.07857.13420.4044
TFTYear=2021, Model Category=Transformer-/decomposition-based baselines2026.0323.43263.47960.51810.10259.11680.5277
NLinearYear=2023, Model Category=Lightweight / linear / graph baselines2026.0345.34964.98761.01560.147513.99080.7563
StemGNNYear=2020, Model Category=Lightweight / linear / graph baselines2026.0354.21915.66011.21420.167415.81520.8583
DeepARYear=2020, Model Category=Transformer-/decomposition-based baselines2026.03452.3120.176100.594157.8873.0595