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Found 7 papers, 5 papers with code
DSAF: A Dual-Stage Adaptive Framework for Numerical Weather Prediction Downscaling
While widely recognized as one of the most substantial weather forecasting methodologies, Numerical Weather Prediction (NWP) usually suffers from relatively coarse resolution and inevitable bias due to tempo-spatial discretization, physical parametrization process, and computation limitation.
WeatherGNN: Exploiting Complicated Relationships in Numerical Weather Prediction Bias Correction
In addition, the interactions between weather factors are further complicated by the spatial dependencies between regions, which are influenced by varied terrain and atmospheric motions.
DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting
Traffic forecasting is essential to intelligent transportation systems, which is challenging due to the complicated spatial and temporal dependencies within a road network.
Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting
On the one hand, our model simultaneously incorporates spatial (node-wise) embeddings and temporal (time-wise) embeddings to account for heterogeneous space-and-time convolutions; on the other hand, it uses GAN structure to systematically evaluate statistical consistencies between the real and the predicted time series in terms of both the temporal trending and the complex spatial-temporal dependencies.
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting
Given the multi-scale feature representations and scale-specific inter-variable dependencies, a multi-scale temporal graph neural network is introduced to jointly model intra-variable dependencies and inter-variable dependencies.
Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting
Time series forecasting is a significant problem in many applications, e. g., financial predictions and business optimization.
Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting
The model constructs a city graph and a city group graph to model the spatial and latent dependencies between cities, respectively.
