  We analyze a cost-minimization problem in which the controller relies on an
imperfect timeseries forecast. Forecasting models generate imperfect forecasts
because they use anonymization noise to protect input data privacy. However,
this noise increases the control cost. We consider a scenario where the
controller pays forecasting models incentives to reduce the noise and combines
the forecasts into one. The controller then uses the forecast to make control
decisions. Thus, forecasting models face a trade-off between accepting
incentives and protecting privacy. We propose an approach to allocate economic
incentives and minimize costs. We solve a biconvex optimization problem on
linear quadratic regulators and compare our approach to a uniform incentive
allocation scheme. The resulting solution reduces control costs by 2.5 and 2.7
times for the synthetic timeseries and the Uber demand forecast, respectively.
