Abstract
A new method is brought forward to predict multivariate time series in this paper. Related time series instead of a single time series are applied to obtain more information about the input signal. The input data are embedded as the phase space points. By the Principle Component Analysis (PCA) the most useful information is extracted form the input signal and the embedding dimension of the phase space is reduced, consequently, the input of the neural networks is simplified. The recurrent neural network has a number of advantages for predicting nonlinear time series. Therefore, Elman neural network is adopted to predict multivariate time series in this paper. Simulations of nonlinear multivariate time series from nature and industry process show the validity of the method proposed.
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Han, M., Fan, M., Xi, J. (2005). Study of Nonlinear Multivariate Time Series Prediction Based on Neural Networks. 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_101
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DOI: https://doi.org/10.1007/11427445_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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