Abstract
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.
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Fontenla-Romero, O., Alonso-Betanzos, A., Castillo, E., Principe, J.C., Guijarro-Berdiñas, B. (2002). Local Modeling Using Self-Organizing Maps and Single Layer Neural Networks. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_153
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DOI: https://doi.org/10.1007/3-540-46084-5_153
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