  Data-driven, model-free analytics are natural choices for discovery and
forecasting of complex, nonlinear systems. Methods that operate in the system
state-space require either an explicit multidimensional state-space, or, one
approximated from available observations. Since observational data are
frequently sampled with noise, it is possible that noise can corrupt the
state-space representation degrading analytical performance. Here, we evaluate
the synthesis of empirical mode decomposition with empirical dynamic modeling,
which we term empirical mode modeling, to increase the information content of
state-space representations in the presence of noise. Evaluation of a
mathematical, and, an ecologically important geophysical application across
three different state-space representations suggests that empirical mode
modeling may be a useful technique for data-driven, model-free, state-space
analysis in the presence of noise.
