Spatio-Temporal Forecasting
34 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Spatio-Temporal Forecasting
Libraries
Use these libraries to find Spatio-Temporal Forecasting models and implementations
Weather2K
Beijing Traffic
Latest papers
SVQ: Sparse Vector Quantization for Spatiotemporal Forecasting
Moreover, we approximate the sparse regression process using a blend of a two-layer MLP and an extensive codebook.
Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset
Air quality forecasting has garnered significant attention recently, with data-driven models taking center stage due to advancements in machine learning and deep learning models.
FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks
In addition, FRIGATE is robust to frugal sensor deployment, changes in road network connectivity, and temporal irregularity in sensing.
Operator Learning with Neural Fields: Tackling PDEs on General Geometries
louisserrano/coral
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NeurIPS 2023
Machine learning approaches for solving partial differential equations require learning mappings between function spaces.
SERT: A Transfomer Based Model for Spatio-Temporal Sensor Data with Missing Values for Environmental Monitoring
aminsn/sert2023
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We propose two models that are capable of performing multivariate spatio-temporal forecasting while handling missing data naturally without the need for imputation.
Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting
zbliu98/gram-ode
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Current works primarily rely on road networks with graph structures and learn representations using graph neural networks (GNNs), but this approach suffers from over-smoothing problem in deep architectures.
Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting
gorgen2020/dvgnn
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Also, by F1-score and probability distribution analysis, we demonstrate that DVGNN better reflects the causal relationship and uncertainty of dynamic graphs.
Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations
To the best of our knowledge, our Weather2K is the first attempt to tackle weather forecasting task by taking full advantage of the strengths of observation data from ground weather stations.
Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction
echo-ji/st-ssl
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ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods.
STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction
echo-ji/stden
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High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities.
