Study of nonlinear multivariate time series prediction based on neural networks

Authors:
Min Han;Mingming Fan;Jianhui Xi
Affiliations:
School of Electronic and Information Engineering, Dalian University of Technology, Dalian, Liaoning, China;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, Liaoning, China;School of Electronic and Information Engineering, Dalian University of Technology, Dalian and Department of Automation, Shenyang Institute of Aeronautical Engineering, Shenyang, Liaoning, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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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.