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A comparative evaluation of nonlinear dynamics methods for time series prediction

Camastra, F. and Filippone, M. (2009) A comparative evaluation of nonlinear dynamics methods for time series prediction. Neural Computing and Applications, 18 (8). pp. 1021-1029. ISSN 0941-0643

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A key problem in time series prediction using autoregressive models is to fix the model order, namely the number of past samples required to model the time series adequately. The estimation of the model order using cross-validation may be a long process. In this paper, we investigate alternative methods to cross-validation, based on nonlinear dynamics methods, namely Grassberger-Procaccia, K,gl, Levina-Bickel and False Nearest Neighbors algorithms. The experiments have been performed in two different ways. In the first case, the model order has been used to carry out the prediction, performed by a SVM for regression on three real data time series showing that nonlinear dynamics methods have performances very close to the cross-validation ones. In the second case, we have tested the accuracy of nonlinear dynamics methods in predicting the known model order of synthetic time series. In this case, most of the methods have yielded a correct estimate and when the estimate was not correct, the value was very close to the real one.

Item Type: Article
Copyright, Publisher and Additional Information: © 2009 Spinger. This is an author produced version of a paper subsequently published in neural computing and applications. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Strange attractors; Model order; Identification; Dimenion
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Miss Anthea Tucker
Date Deposited: 30 Oct 2009 09:54
Last Modified: 08 Feb 2013 16:59
Published Version: http://dx.doi.org/10.1007/s00521-009-0266-y
Status: Published
Publisher: Springer Verlag
Refereed: Yes
Identification Number: 10.1007/s00521-009-0266-y
URI: http://eprints.whiterose.ac.uk/id/eprint/10045

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