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
Most implemented papers
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks
martenlienen/finite-element-networks
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ICLR 2022
We propose a new method for spatio-temporal forecasting on arbitrarily distributed points.
Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention
swsamleo/mlstgcn
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To address these issues, we construct new graph models to represent the contextual information of each node and the long-term spatio-temporal data dependency structure.
AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs
We present the first whiteness test for graphs, i. e., a whiteness test for multivariate time series associated with the nodes of a dynamic graph.
Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures
Various alterations have been proposed to better facilitate time series forecasting, of which this study focused on the Informer, LogSparse Transformer and Autoformer.
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.
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.
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.
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.
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.
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.
