Empirical Dynamic Modeling offers a flexible complement to standard mechanistic modeling approaches. Because it makes no assumptions concerning the structural form of ecological processes, it can provide an effective approach to dealing with model uncertainty. The method uses non-linear, non-parametric models. It can accommodate a wide spectrum of dynamical behaviors and makes no equilibrium assumptions. The approach is predicated on the idea that within a time series of observations of an ecological variable (e.g. population or species abundance) is encoded information on the factors that have affected that variable over time (e.g. the effects of predators or prey, competitors, environmental change, etc.). The method employs state-space reconstruction to decode this embedded information, and applies nearest-neighbor and kernel regression methods of forecasting. Forecast skill is used directly as a criterion for model selection and validation. It has been proven effective in application to fisheries forecasting problems, often outperforming standard modeling approaches.
General solution for the dynamic modeling of parallel robots
