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Found 6 papers, 4 papers with code
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
The input time series are progressively coarsened over time and space, obtaining a pool of representations that capture heterogeneous temporal and spatial dynamics.
Graph Deep Learning for Time Series Forecasting
The conditioning can take the form of an architectural inductive bias on the neural forecasting architecture, resulting in a family of deep learning models called spatiotemporal graph neural networks.
Taming Local Effects in Graph-based Spatiotemporal Forecasting
Spatiotemporal graph neural networks have shown to be effective in time series forecasting applications, achieving better performance than standard univariate predictors in several settings.
Scalable Spatiotemporal Graph Neural Networks
The training procedure can then be parallelized node-wise by sampling the node embeddings without breaking any dependency, thus enabling scalability to large networks.
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
In particular, we propose a novel class of attention-based architectures that, given a set of highly sparse discrete observations, learn a representation for points in time and space by exploiting a spatiotemporal propagation architecture aligned with the imputation task.
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
In particular, we introduce a novel graph neural network architecture, named GRIN, which aims at reconstructing missing data in the different channels of a multivariate time series by learning spatio-temporal representations through message passing.
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