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The FNN algorithm and noise corrupted time series


C. Rhodes, M. Morari

vol. AUT96-03

The false nearest neighbors (FNN) algorithm was originally developed to determine the embedding dimension for autonomous time series. For noise free computer generated time series, the algorithm does a good job in predicting the embedding dimension. However the problem of predicting the embedding dimension when the time series data is corrupted by noise was not fully examined in the original studies of the FNN algorithm. Here it is shown that with large data sets, even small amounts of noise can lead to incorrect prediction of the embedding dimension. Surprisingly as the length of the time series analyzed by FNN grows larger, the cause of incorrect prediction becomes more pronounced. An analysis of the effect of noise on the FNN algorithm, and a solution for dealing with the effects of noise are given here. Some results on the theoretically correct choice of the FNN threshold are also presented.


Type of Publication:

(04)Technical Report

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% Autogenerated BibTeX entry
@TechReport { RhoMor:1996:IFA_1491,
    author={C. Rhodes and M. Morari},
    title={{The FNN algorithm and noise corrupted time series}},
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