Naive time-series forecast
= 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.