Self-organizing maps and functional networks for local dynamic modeling

The paper presents a method for times series prediction using a local dynamic modeling based on a three step process. In the first step the input data is embedded in a reconstruction space using a memory structure. The second step, implemented by a self-organizing map (SOM), derives a set of local models from data. The third step is accomplished by a set of functional networks. The goal of the last network is to fit a local model from the winning neuron and a set of neighbors of the SOM map. Finally, the performance of the proposed method was validated using two chaotic time series.