It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting

TL;DR

This paper introduces a novel graph-based framework for spatio-temporal forecasting that captures both spatial and temporal dependencies simultaneously, effectively handling irregular data without imputation.

Contribution

The proposed framework models each sample as a node, enabling joint learning of spatial and temporal dependencies with GNNs, and supports irregular time series without data imputation.

Findings

Outperformed existing models in wind speed forecasting

Effectively handles irregular and missing data

Simplifies model architecture by joint learning of dependencies

Abstract

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.