  In this paper I present a new approach for regression of time series using
their own samples. This is a celebrated problem known as Auto-Regression.
Dealing with outlier or missed samples in a time series makes the problem of
estimation difficult, so it should be robust against them. Moreover for coding
purposes I will show that it is desired the residual of auto-regression be
sparse. To these aims, I first assume a multivariate Gaussian prior on the
residual and then obtain the estimation. Two simple simulations have been done
on spectrum estimation and speech coding.
