A comparative evaluation of nonlinear dynamics methods for time series prediction

Francesco Camastra*, Maurizio Filippone

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1021-1029
Number of pages9
JournalNeural Computing and Applications
Issue number8
StatePublished - Oct 2009

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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