AirQualityBench is a realistic global benchmark using hourly data from 3720 stations across 2021-2025 for six pollutants, preserving native missingness masks and evaluating on inverse-transformed physical scales.
Decoupled dynamic spatial-temporal graph neural network for traffic forecasting
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
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2026 6roles
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PHGNet proposes prototype-guided hypergraph construction plus global-local representations and temporal attention to model high-order spatiotemporal dependencies in traffic data and reports better performance than prior methods on real datasets.
UniSTOK improves inductive spatio-temporal kriging under incomplete observations by reliability-guided signal regulation and residual bias calibration.
ADMFormer decouples traffic into regular and fluctuating components with time-node gating, processes them in dual temporal branches, and uses time-varying masked spatial attention to reach SOTA on four datasets.
TSNN matches time series entries to a training-derived memory bank to forecast traffic without any trainable parameters and achieves competitive accuracy on four real-world datasets.
RCSNet is a road-conditioned spatiotemporal network for traffic movie prediction that reports 5-11% metric gains over baselines in same-city and cross-city settings.
citing papers explorer
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AirQualityBench: A Realistic Evaluation Benchmark for Global Air Quality Forecasting
AirQualityBench is a realistic global benchmark using hourly data from 3720 stations across 2021-2025 for six pollutants, preserving native missingness masks and evaluating on inverse-transformed physical scales.
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PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting
PHGNet proposes prototype-guided hypergraph construction plus global-local representations and temporal attention to model high-order spatiotemporal dependencies in traffic data and reports better performance than prior methods on real datasets.
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Uniform Inductive Spatio-Temporal Kriging
UniSTOK improves inductive spatio-temporal kriging under incomplete observations by reliability-guided signal regulation and residual bias calibration.
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ADMFormer: An Adaptive-Decomposition Transformer with Time-Varying Masked Spatial Attention for Traffic Forecasting
ADMFormer decouples traffic into regular and fluctuating components with time-node gating, processes them in dual temporal branches, and uses time-varying masked spatial attention to reach SOTA on four datasets.
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TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting
TSNN matches time series entries to a training-derived memory bank to forecast traffic without any trainable parameters and achieves competitive accuracy on four real-world datasets.
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A Road-Conditioned Traffic Movie Prediction Network with Spatiotemporal and Structure-Consistent Learning
RCSNet is a road-conditioned spatiotemporal network for traffic movie prediction that reports 5-11% metric gains over baselines in same-city and cross-city settings.