We consider the convergence of moving averages in the general setting of ergodic theory or stationary ergodic processes. We characterize when there is universal convergence of moving averages based on complete convergence to zero of the standard ergodic averages. Using a theorem of Hsu-Robbins (1947) for independent, identically distributed processes, we prove for any bounded measurable function $f$ on a standard probability space $(X,\mathcal{B},μ)$, there exists a Bernoulli shift $T$, such that all moving averages $M(v_n, L_n)^T f = \frac{1}{L_n} \sum_{i=v_n+1}^{v_n+L_n} f \circ T^i$ with $L_n\geq n$ converge a.e. to $\int_X f dμ$. We refresh the reader about the cone condition established by Bellow, Jones, Rosenblatt (1990) which guarantees convergence of certain moving averages for all $f \in L^1(μ)$ and ergodic measure preserving maps $T$. We show given $f \in L^1(μ)$ and ergodic measure preserving $T$, there exists a moving average $M(v_n,L_n)^T f$ with $L_n$ strictly increasing such that $(v_n,L_n)$ does not satisfy the cone condition, but pointwise convergence holds a.e. We show for any non-zero $f\in L^1(μ)$, there is a generic class of ergodic maps $T$ such that each map has an associated moving average $M(v_n, L_n)^T f$ which does not converge pointwise. It is known if $f\in L^2(μ)$ is mean-zero, then there exist solutions $T$ and $g\in L^1(μ)$ to the coboundary equation: $f = g - g\circ T$. This implies $f$ and $T$ produce universal moving averages. We show this does not generalize to $L^p(μ)$ for $p<2$ by explicitly defining functions $f\in \cap_{p<2}L^p(μ)$ such that for each ergodic measure preserving $T$, there exists a moving average $M(v_n, L_n)^T f$ with $L_n\geq n$ such that these moving averages do not converge pointwise. Several of the results are generalized to the case of moving averages with polynomial growth.