Abstract
Nonlinear time series modeling and forecasting is one of important problems of nonlinear time series analysis. In this paper, we prove that projection pursuit learning network can approximate to nonlinear autoregression at any given precision in L k space, where k is some positive integer. The mathematical formulation, training strategy and calculative procedures are also presented. The results of application to real data show that the projection pursuit learning network outperforms the traditional methods.
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© 2005 Springer-Verlag Berlin Heidelberg
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Tian, Z., Jin, Z., He, F., Ling, W. (2005). The Projection Pursuit Learning Network for Nonlinear Time Series Modeling and Forecasting. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_105
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DOI: https://doi.org/10.1007/11427445_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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