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Traffic Prediction On Pems07

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MAE@1h

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이 벤치마크에서 각 모델의 성능 결과

모델 이름
MAE@1h
Paper TitleRepository
ADCSD19.62Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting
STGODE22.99Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
PDG2Seq19.28PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction
STWave19.94When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks
PM-DMNet(P)19.35Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction
STD-MAE18.31Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
ASTGCN28.05Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
PDFormer19.83PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
ADN21.62Structured Time Series Prediction without Structural Prior
DDGCRN19.79A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting
CorrSTN19.62A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting
STAEformer19.14STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
STGCN25.38Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
STSGCN24.26Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
Cy2Mixer19.45Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks
STFGNN22.07Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting
PM-DMnet(R)19.18Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction
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