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
Prediction-based One-shot Dynamic Parking Pricing
seoyoungh/one-shot-optimization
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Owing to the continuous and bijective characteristics of NODEs, in addition, we design a one-shot price optimization method given a pre-trained prediction model, which requires only one iteration to find the optimal solution.
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.
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.
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.
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.
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.
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.
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.
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.
