A Novel System-Based Empirical Mode Decomposition with Improved Upper Bounds Applied to Environmental Datasets

We are interested in modelling smaller datasets to generate more accurate, sub-regional or regional climate forecasts. The focus of this paper is to present the findings of a study investigating the application of empirical mode decomposition (EMD) to identify the components of the signal from which we can subsequently derive an iterated function system (IFS). One could develop a series of models, which are not based on big data, but rather allow for a cyclical model to keep the cycle iterating so that the model can be more responsive and adaptive to changes in the climate. The results presented in this paper have identified a new upper bound for the number of intrinsic mode functions (IMFs) obtained after EMD. The goal of the research is to develop a model where climate data could be iterated adaptively between models.