Please use this identifier to cite or link to this item: https://doi.org/10.1063/1.2130927
Title: Error criteria for cross validation in the context of chaotic time series prediction
Authors: Lim, T.P.
Puthusserypady, S. 
Issue Date: 2006
Source: Lim, T.P., Puthusserypady, S. (2006). Error criteria for cross validation in the context of chaotic time series prediction. Chaos 16 (1) : -. ScholarBank@NUS Repository. https://doi.org/10.1063/1.2130927
Abstract: The prediction of a chaotic time series over a long horizon is commonly done by iterating one-step-ahead prediction. Prediction can be implemented using machine learning methods, such as radial basis function networks. Typically, cross validation is used to select prediction models based on mean squared error. The bias-variance dilemma dictates that there is an inevitable tradeoff between bias and variance. However, invariants of chaotic systems are unchanged by linear transformations; thus, the bias component may be irrelevant to model selection in the context of chaotic time series prediction. Hence, the use of error variance for model selection, instead of mean squared error, is examined. Clipping is introduced, as a simple way to stabilize iterated predictions. It is shown that using the error variance for model selection, in combination with clipping, may result in better models. © 2006 American Institute of Physics.
Source Title: Chaos
URI: http://scholarbank.nus.edu.sg/handle/10635/55908
ISSN: 10541500
DOI: 10.1063/1.2130927
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

3
checked on Dec 5, 2017

WEB OF SCIENCETM
Citations

3
checked on Dec 5, 2017

Page view(s)

23
checked on Dec 18, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.