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Nonlinear time‐series modeling and prediction using correlation analysis
Author(s) -
Dydyński Andrzej,
Arabas Jaroslaw
Publication year - 2007
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.200700201
Subject(s) - perceptron , series (stratigraphy) , nonlinear system , radial basis function , correlation , computer science , algorithm , multilayer perceptron , mean squared prediction error , time series , artificial intelligence , character (mathematics) , mathematics , artificial neural network , machine learning , physics , quantum mechanics , biology , paleontology , geometry
We present a novel way for time series prediction. The method is based on the correlation analysis and allows for handling nonlinearities of different type and character. The presented approach results in an approximation model that combines nonlinear units taken from radial basis functions (RBF) and from multilayer perceptrons (MLP). The approach leads to a low mean error of the approximation with a number of parameters significantly smaller when compared to RBF and MLP. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)