  Stationary processes have been extensively studied in the literature. Their
applications include modeling and forecasting numerous real life phenomena such
as natural disasters, sales and market movements. When stationary processes are
considered, modeling is traditionally based on fitting an autoregressive moving
average (ARMA) process. However, we challenge this conventional approach.
Instead of fitting an ARMA model, we apply an AR(1) characterization in
modeling any strictly stationary processes. Moreover, we derive consistent and
asymptotically normal estimators of the corresponding model parameter.
