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Traffic Prediction On Pems04

Metrics

12 Steps MAE

Results

Performance results of various models on this benchmark

		Paper Title	Repository
AGCRN	19.83	Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
IDCN	19.33	-	-
FasterSTS	18.49	FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting
DDGCRN	18.45	A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting	-
PDFormer	18.32	PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
PDG2Seq	18.24	PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction	-
STAEformer	18.22	STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
Cy2Mixer	18.14	Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks
DTRformer	18	Dynamic Trend Fusion Module for Traffic Flow Prediction
HTVGNN	17.99	A novel hybrid time-varying graph neural network for traffic flow forecasting	-
STD-MAE	17.80	Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
LightCTS	-	LightCTS: A Lightweight Framework for Correlated Time Series Forecasting

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Traffic Prediction On Pems04 | SOTA | HyperAI