We compared the ensemble RMSEs obtained by each generalised MSER method to the minimum possible RMSE obtained by applying the optimal fixed-time truncation to all time series. In general, methods which produced larger variance of the mean estimates were prone to choosing late truncation points, increasing SD Trajs (⟨∆G⟩ [n 0 ,N ] ), while methods which underestimated the variance of the mean were prone to choosing early truncation points, increasing Bias Trajs (⟨∆G⟩ [n 0 ,N ] ). This is illustrated for one of the synthetic MIF time series in were often caused by sudden decreases in the maximum lag index used to calculate the autocovariance sum. For the Chodera method in particular, troughs at single values of n 0 were often observed (Figure ). However, issues with troughs in the maximum lag index were generally removed or reduced with increasingly stringent initial sequence methods. In general, Geyer's initial monotone and convex sequence methods produced the smoothest traces of Var Trajs (⟨∆G⟩ [n 0 ,N ] ) of the "initial sequence" methods (including Chodera's). The window method with window size N n 0 compromises the extremes of the uncorrelated estimate and the initial sequence methods by including enough treatment of correlation to avoid excessively early truncation, while avoiding noisy variance of the mean estimates which can produce late truncation. More quantitatively, the performance of all methods across the "standard" synthetic ensembles is compared in Figure and Table . The test systems can be divided into two groups: those where the optimal fixed-time truncation point is around or above 50 % of the simulation time (MIF, MDM2-Nutlin, and MDM2-Pip2), and those where it is substantially less (T4L, PDE2a). Unsurprisingly, methods prone to late truncation (the initial sequence methods), performed best in the first set, while methods prone to early truncation performed best in the latter set. For example, MDM2-Pip2 had a relatively late optimal fixed truncation time (around 4 ns). The uncorrelated estimate method generally truncated at less than 1 ns for this system, producing an ensemble RMSE around 4 times greater than the fixed truncation point minimum. For the window estimators, the mean truncation times increased and the ensemble RMSEs decreased with increasing window size up to the N n 0 window estimator -this produced an ensemble RMSD close to the optimal fixed truncation time limit and to the initial sequence estimator methods, which performed the best. In contrast, the PDE2a system had a relatively early optimal fixed truncation time (around 1.5 ns).