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홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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Traffic Prediction On Pems04

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12 Steps MAE

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이 벤치마크에서 각 모델의 성능 결과

모델 이름
12 Steps MAE
Paper TitleRepository
IDCN19.33--
PDG2Seq18.24PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction
STAEformer18.22STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
Cy2Mixer18.14Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks
STD-MAE17.80Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
DDGCRN18.45A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting
HTVGNN17.99A novel hybrid time-varying graph neural network for traffic flow forecasting-
FasterSTS18.49FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting-
PDFormer18.32PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
LightCTS-LightCTS: A Lightweight Framework for Correlated Time Series Forecasting
DTRformer18Dynamic Trend Fusion Module for Traffic Flow Prediction
AGCRN19.83Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
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