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Traffic Prediction On Pems04

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12 Steps MAE

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	12 Steps MAE	Paper Title	Repository
IDCN	19.33	-	-
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
STD-MAE	17.80	Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
DDGCRN	18.45	A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting
HTVGNN	17.99	A novel hybrid time-varying graph neural network for traffic flow forecasting	-
FasterSTS	18.49	FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting	-
PDFormer	18.32	PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
LightCTS	-	LightCTS: A Lightweight Framework for Correlated Time Series Forecasting
DTRformer	18	Dynamic Trend Fusion Module for Traffic Flow Prediction
AGCRN	19.83	Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

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Traffic Prediction On Pems04 | SOTA | HyperAI