Abstract
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.
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References
Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47–97 (2002)
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: Structure and dynamics. Physics Reports 424, 175–308 (2006)
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Barreto, G.A., Araújo, A.F.R.: Identification and control of dynamical systems using the self-organizing map. IEEE Transactions on Neural Networks 15(5), 1244–1259 (2004)
Barreto, G.A.: Time Series Prediction with the Self-Organizing Map: A Review. Perspectives of Neural-Symbolic Integration, Studies in Computational Intelligence 77, 135–158 (2007)
Erdos, P., Renyi, A.: On random graphs. Publicationes Mathematicae 6, 290–297 (1959)
Van Hulle, M.M.: Self-organizing maps: Theory, design, and application, Tokyo (2001)
Jiang, F., Berry, H., Schoenauer, M.: The Impact of Network Topology on Self-Organizing Maps. In: GEC 2009, Shanghai, China (2009)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer (2001)
Mackey, M.C., Glass, J.: Oscillation and chaos in physiological control systems. Science 197, 287 (1977)
Palit, A.K., Popovic, D.: Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications, 1st edn. Springer (2005)
Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393, 440–442 (1998)
Weigend, A., Gershefeld, N.: Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley (1993)
Yang, S., Luo, S.-W., Li, J.: An extended model on self-organizing map. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4232, pp. 987–994. Springer, Heidelberg (2006)
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Burguillo, J.C., Dorronsoro, B. (2013). Using Complex Network Topologies and Self-Organizing Maps for Time Series Prediction. In: Zelinka, I., Chen, G., Rössler, O., Snasel, V., Abraham, A. (eds) Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Advances in Intelligent Systems and Computing, vol 210. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00542-3_33
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DOI: https://doi.org/10.1007/978-3-319-00542-3_33
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00541-6
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