  Knowledge about the hidden factors that determine particular system dynamics
is crucial for both explaining them and pursuing goal-directed interventions.
Inferring these factors from time series data without supervision remains an
open challenge. Here, we focus on spatiotemporal processes, including wave
propagation and weather dynamics, for which we assume that universal causes
(e.g. physics) apply throughout space and time. A recently introduced
DIstributed SpatioTemporal graph Artificial Neural network Architecture
(DISTANA) is used and enhanced to learn such processes, requiring fewer
parameters and achieving significantly more accurate predictions compared to
temporal convolutional neural networks and other related approaches. We show
that DISTANA, when combined with a retrospective latent state inference
principle called active tuning, can reliably derive location-respective hidden
causal factors. In a current weather prediction benchmark, DISTANA infers our
planet's land-sea mask solely by observing temperature dynamics and, meanwhile,
uses the self inferred information to improve its own future temperature
predictions.
