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|Title:||Chaotic time series prediction and additive white Gaussian noise|
Time series prediction
|Citation:||Lim, T.P., Puthusserypady, S. (2007-06-04). Chaotic time series prediction and additive white Gaussian noise. Physics Letters, Section A: General, Atomic and Solid State Physics 365 (4) : 309-314. ScholarBank@NUS Repository. https://doi.org/10.1016/j.physleta.2007.01.027|
|Abstract:||Taken's delay embedding theorem states that a pseudo state-space can be reconstructed from a time series consisting of observations of a chaotic process. However, experimental observations are inevitably corrupted by measurement noise, which can be modelled as Additive White Gaussian Noise (AWGN). This Letter analyses time series prediction in the presence of AWGN using the triangle inequality and the mean of the Nakagami distribution. It is shown that using more delay coordinates than those used by a typical delay embedding can improve prediction accuracy, when the mean magnitude of the input vector dominates the mean magnitude of AWGN. © 2007 Elsevier B.V. All rights reserved.|
|Source Title:||Physics Letters, Section A: General, Atomic and Solid State Physics|
|Appears in Collections:||Staff Publications|
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