Here we present a novel time-dependent correlation method that provides insight into how long a system takes to grow into its equal-time (Pearson) correlation. We also show a novel usage of an extant time-lagged correlation method that indicates the time for parts of a system to become decorrelated, relative to equal-time correlation. Given a completed simulation (or set of simulations), these tools estimate (1) how long of a simulation of the same system would be sufficient to observe the same

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... correlated motions, (2) if patterns of observed correlated motions indicate events beyond the timescale of the simulation, and (3) how long of a simulation is needed to observe these longer timescale events. We view this method as a decision-support tool that will aid researchers in determining necessary sampling times. In principle, this tool is extendable to any multi-dimensional time series data with a notion of correlated fluctuations; however, here we limit our discussion to data from Molecular Dynamics simulations.