Forecasting Unobserved Node States with spatio-temporal Graph Neural Networks

by   Andreas Roth, et al.

Forecasting future states of sensors is key to solving tasks like weather prediction, route planning, and many others when dealing with networks of sensors. But complete spatial coverage of sensors is generally unavailable and would practically be infeasible due to limitations in budget and other resources during deployment and maintenance. Currently existing approaches using machine learning are limited to the spatial locations where data was observed, causing limitations to downstream tasks. Inspired by the recent surge of Graph Neural Networks for spatio-temporal data processing, we investigate whether these can also forecast the state of locations with no sensors available. For this purpose, we develop a framework, named Forecasting Unobserved Node States (FUNS), that allows forecasting the state at entirely unobserved locations based on spatio-temporal correlations and the graph inductive bias. FUNS serves as a blueprint for optimizing models only on observed data and demonstrates good generalization capabilities for predicting the state at entirely unobserved locations during the testing stage. Our framework can be combined with any spatio-temporal Graph Neural Network, that exploits spatio-temporal correlations with surrounding observed locations by using the network's graph structure. Our employed model builds on a previous model by also allowing us to exploit prior knowledge about locations of interest, e.g. the road type. Our empirical evaluation of both simulated and real-world datasets demonstrates that Graph Neural Networks are well-suited for this task.


page 1

page 2

page 3

page 4


Graph Neural Processes for Spatio-Temporal Extrapolation

We study the task of spatio-temporal extrapolation that generates data a...

Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling

Vast amount of data generated from networks of sensors, wearables, and t...

INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

Spatio-temporal kriging is an important problem in web and social applic...

DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models

Spatio-temporal graph neural networks (STGNN) have emerged as the domina...

FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks

Modelling spatio-temporal processes on road networks is a task of growin...

Fast Temporal Wavelet Graph Neural Networks

Spatio-temporal signals forecasting plays an important role in numerous ...

Uncertainty-aware Traffic Prediction under Missing Data

Traffic prediction is a crucial topic because of its broad scope of appl...

Please sign up or login with your details

Forgot password? Click here to reset