The estimation of entropy rates for stationary discrete-valued stochastic processes is a well studied problem in information theory. However, estimating the entropy rate for stationary continuous-valued stochastic processes has not received as much attention. In fact, many current techniques are not able to accurately estimate or characterise the complexity of the differential entropy rate for strongly correlated processes, such as Fractional Gaussian Noise and ARFIMA(0,d,0). To the point that some cannot even detect the trend of the entropy rate, e.g. when it increases/decreases, maximum, or asymptotic trends, as a function of their Hurst parameter. However, a recently developed technique provides accurate estimates at a high computational cost. In this paper, we define a robust technique for non-parametrically estimating the differential entropy rate of a continuous valued stochastic process from observed data, by making an explicit link between the differential entropy rate and the Shannon entropy rate of a quantised version of the original data. Estimation is performed by a Shannon entropy rate estimator, and then converted to a differential entropy rate estimate. We show that this technique inherits many important statistical properties from the Shannon entropy rate estimator. The estimator is able to provide better estimates than the defined relative measures and much quicker estimates than known absolute measures, for strongly correlated processes. Finally, we analyse the complexity of the estimation technique and test the robustness to non-stationarity, and show that none of the current techniques are robust to non-stationarity, even if they are robust to strong correlations.