  We consider a setting, where the output of a linear dynamical system (LDS)
is, with an unknown but fixed probability, replaced by noise. There, we present
a robust method for the prediction of the outputs of the LDS and identification
of the samples of noise, and prove guarantees on its statistical performance.
One application lies in anomaly detection: the samples of noise, unlikely to
have been generated by the dynamics, can be flagged to operators of the system
for further study.
