Based on the modern time-series analysis method, a new time-domain Wiener filtering approach is presented. Asymptotically stable Wiener state estimators are presented for discrete linear stochastic descriptor systems. They can be implemented via the autoregressive moving average (ARMA) recursive filters. They can handle the optimal state filtering, smoothing, and prediction problems in a unified framework, and can simply be obtained based on the ARMA innovation model. The solution of the Diophantine equations and Riccati equations is avoided, so that the computational burden is reduced. A simulation example shows the effectiveness of the new approach.