  Sharing forecasts of network timeseries data, such as cellular or electricity
load patterns, can improve independent control applications ranging from
traffic scheduling to power generation. Typically, forecasts are designed
without knowledge of a downstream controller's task objective, and thus simply
optimize for mean prediction error. However, such task-agnostic representations
are often too large to stream over a communication network and do not emphasize
salient temporal features for cooperative control. This paper presents a
solution to learn succinct, highly-compressed forecasts that are co-designed
with a modular controller's task objective. Our simulations with real cellular,
Internet-of-Things (IoT), and electricity load data show we can improve a model
predictive controller's performance by at least $25\%$ while transmitting
$80\%$ less data than the competing method. Further, we present theoretical
compression results for a networked variant of the classical linear quadratic
regulator (LQR) control problem.
