Spatio-Temporal Forecasting
34 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Libraries
Use these libraries to find Spatio-Temporal Forecasting models and implementations
Weather2K
Beijing Traffic
Most implemented papers
Enhanced spatio-temporal electric load forecasts using less data with active deep learning
ArsamAryandoust/DataSelectionMaps
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We show how electric utilities can apply active learning to better distribute smart meters and collect their data for more accurate predictions of load with about half the data compared to when applying passive learning.
Conditional Local Convolution for Spatio-temporal Meteorological Forecasting
bird-tao/clcrn
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We further propose the distance and orientation scaling terms to reduce the impacts of irregular spatial distribution.
SG-PALM: a Fast Physically Interpretable Tensor Graphical Model
We propose a new graphical model inference procedure, called SG-PALM, for learning conditional dependency structure of high-dimensional tensor-variate data.
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
Vast amount of data generated from networks of sensors, wearables, and the Internet of Things (IoT) devices underscores the need for advanced modeling techniques that leverage the spatio-temporal structure of decentralized data due to the need for edge computation and licensing (data access) issues.
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks.
Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand
fbohu/Deep-Spatio-Temporal-Forecasting-of-Electrical-Vehicle-Charging-Demand
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To meet this requirement, accurate forecasting of the charging demand is vital.
LibCity: An Open Library for Traffic Prediction
libcity/bigscity-libcity
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International Conference on Advances in Geographic Information Systems 2021
This paper presents LibCity, a unified, comprehensive, and extensible library for traffic prediction, which provides researchers with a credible experimental tool and a convenient development framework.
Graph Neural Controlled Differential Equations for Traffic Forecasting
A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing.
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities
yihongt/dastnet
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To the best of our knowledge, we are the first to employ adversarial multi-domain adaptation for network-wide traffic forecasting problems.
On the importance of stationarity, strong baselines and benchmarks in transport prediction problems
Over the last years, the transportation community has witnessed a tremendous amount of research contributions on new deep learning approaches for spatio-temporal forecasting.
