  We describe an optimal adversarial attack formulation against autoregressive
time series forecast using Linear Quadratic Regulator (LQR). In this threat
model, the environment evolves according to a dynamical system; an
autoregressive model observes the current environment state and predicts its
future values; an attacker has the ability to modify the environment state in
order to manipulate future autoregressive forecasts. The attacker's goal is to
force autoregressive forecasts into tracking a target trajectory while
minimizing its attack expenditure. In the white-box setting where the attacker
knows the environment and forecast models, we present the optimal attack using
LQR for linear models, and Model Predictive Control (MPC) for nonlinear models.
In the black-box setting, we combine system identification and MPC. Experiments
demonstrate the effectiveness of our attacks.
