  We propose directed time series regression, a new approach to estimating
parameters of time-series models for use in certainty equivalent model
predictive control. The approach combines merits of least squares regression
and empirical optimization. Through a computational study involving a
stochastic version of a well known inverted pendulum balancing problem, we
demonstrate that directed time series regression can generate significant
improvements in controller performance over either of the aforementioned
alternatives.

Machine Learning;Systems and Control;Machine Learning