It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting
This addresses the problem of handling irregular or missing data in spatio-temporal forecasting for applications like sensor networks, but it is incremental as it builds on existing GNN methods.
The paper tackles spatio-temporal forecasting by proposing a new graph formulation that encodes each recorded sample as a node, allowing Graph Neural Networks to jointly learn spatial and temporal dependencies without separate temporal networks. It outperforms existing models in wind speed forecasting, though specific numerical gains are not provided.
With an ever-increasing number of sensors in modern society, spatio-temporal time series forecasting has become a de facto tool to make informed decisions about the future. Most spatio-temporal forecasting models typically comprise distinct components that learn spatial and temporal dependencies. A common methodology employs some Graph Neural Network (GNN) to capture relations between spatial locations, while another network, such as a recurrent neural network (RNN), learns temporal correlations. By representing every recorded sample as its own node in a graph, rather than all measurements for a particular location as a single node, temporal and spatial information is encoded in a similar manner. In this setting, GNNs can now directly learn both temporal and spatial dependencies, jointly, while also alleviating the need for additional temporal networks. Furthermore, the framework does not require aligned measurements along the temporal dimension, meaning that it also naturally facilitates irregular time series, different sampling frequencies or missing data, without the need for data imputation. To evaluate the proposed methodology, we consider wind speed forecasting as a case study, where our proposed framework outperformed other spatio-temporal models using GNNs with either Transformer or LSTM networks as temporal update functions.