  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.
