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. (doi: 10.1007/s00521-009-0266-y)

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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, Kégl, 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:Articles
Glasgow Author(s) Enlighten ID:Filippone, Dr Maurizio
Authors: Camastra, F., and Filippone, M.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Neural Computing and Applications
ISSN (Online):1433-3058
Published Online:29 April 2009

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