Local modeling using self-organizing maps and single layer neural networks

The paper presents a method for time series prediction using local dynamic modeling. After embedding the input data in a reconstruction space using a memory structure, a self-organizing map (SOM) derives a set of local models from these data. Afterwards, a set of single layer neural networks, trained optimally with a system of linear equations, is applied at the SOM's output. The goal of the last network is to, fit a local model from the winning neuron and a set of neighbours of the SOM map. Finally, the performance of the proposed method was validated using two chaotic time series.