Please use this identifier to cite or link to this item:
|Title:||Chaotic time series prediction and additive white Gaussian noise|
Time series prediction
|Source:||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|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 7, 2017
WEB OF SCIENCETM
checked on Nov 29, 2017
checked on Dec 11, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.