Naive time-series forecast

A naive forecast repeats the latest observation at every future horizon. For a stationary mean-$\mu$ process its horizon-$h$ mean squared prediction error is $2[\gamma (0)-\gamma (h)]$. A forecast of the known mean has risk $\gamma (0)$, so persistence is not automatically better when lagged dependence is weak.