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Published in:
2013 | OriginalPaper | Chapter
Using Complex Network Topologies and Self-Organizing Maps for Time Series Prediction
Authors : Juan C. Burguillo, Bernabé Dorronsoro
Published in: Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems
Publisher: Springer International Publishing
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A Self-organizing Map (SOM) is a competitive learning neural network architecture that make available a certain amount of classificatory neurons, which self-organize spatially based on input patterns. In this paper we explore the use of complex network topologies, like small-world, scale-free or random networks; for connecting the neurons within a SOM, and apply them for Time Series Prediction (TSP).We follow the classical VQTAMmodel for function prediction, and consider several benchmarks to evaluate the quality of the predictions. The results presented in this work suggest that the most regular the network topology is, the better results it provides in prediction. Besides, we have found that not updating all the cells at the same time provides much better results.