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Data-driven, model-free analytics are natural choices for discovery andforecasting of complex, nonlinear systems. Methods that operate in the systemstate-space require either an explicit multidimensional state-space, or, oneapproximated from available observations. Since observational data arefrequently sampled with noise, it is possible that noise can corrupt thestate-space representation degrading analytical performance. Here, we evaluatethe synthesis of empirical mode decomposition with empirical dynamic modeling,which we term empirical mode modeling, to increase the information content ofstate-space representations in the presence of noise. Evaluation of amathematical, and, an ecologically important geophysical application acrossthree different state-space representations suggests that empirical modemodeling may be a useful technique for data-driven, model-free, state-spaceanalysis in the presence of noise.

Empirical Mode Modeling: A data-driven approach to recover and forecast nonlinear dynamics from noisy data