Spatio-Temporal Forecasting
34 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Libraries
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Weather2K
Beijing Traffic
Latest papers with no code
HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting
In this paper, we propose a novel Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to model cross-regional spatio-temporal correlations among meteorological variables in multiple stations.
Spatio-temporal Parking Behaviour Forecasting and Analysis Before and During COVID-19
Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours.
Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems
To tackle these challenges, we advocate a spatio-temporal physics-coupled neural networks (ST-PCNN) model to learn the underlying physics of the dynamical system and further couple the learned physics to assist the learning of the recurring dynamics.
Gated Ensemble of Spatio-temporal Mixture of Experts for Multi-task Learning in Ride-hailing System
Designing spatio-temporal forecasting models separately in a task-wise and city-wise manner poses a burden for the expanding transportation network companies.
Spatio-Temporal Forecasting With Gridded Remote Sensing Data Using Feed-Backward Decoding
We present a novel deep learning approach for spatio-temporal forecasting with remote sensing data, extending a previous model named Spatio-Temporal Convolutional Sequence to Sequence Network (STConvS2S) in several directions.
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting
Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting.
A data-driven approach to the forecasting of ground-level ozone concentration
The ability to forecast the concentration of air pollutants in an urban region is crucial for decision-makers wishing to reduce the impact of pollution on public health through active measures (e. g. temporary traffic closures).
Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting
Recently, a few Seq2Seq based approaches have been proposed, but one of the drawbacks of Seq2Seq models is that, small errors can accumulate quickly along the generated sequence at the inference stage due to the different distributions of training and inference phase.
CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction
Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance.
HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting
In this paper, we propose a general framework (HyperST-Net) based on hypernetworks for deep ST models.
