This manuscript focuses on the impact of adversarial perturbations on forecasts where an attacker manipulates a batch of data before it is observed by the defender. The proposed Bayesian decision models are based on adversarial risk analysis allowing incomplete information. We demonstrate the proposed framework using hidden Markov Models, and discuss potential applications.
