NDLI: Data partition and variable selection for time series prediction using wrappers

Content Provider	IEEE Xplore Digital Library
Author	Puma-Villanueva, W.J. dos Santos, E.P. Von Zuben, F.J.
Copyright Year	2006
Description	Author affiliation: Univ. of Campinas, Campinas (Puma-Villanueva, W.J.; dos Santos, E.P.; Von Zuben, F.J.)
Abstract	The purpose of this paper is a comparative study of a non-exhaustive, though representative, set of methodologies already available for the partition of the training dataset in time series prediction, and also for variable selection under the wrapper paradigm. The partition policy of the training dataset and the choice of a proper set of variables for the regression vector are known to have a significant influence in the accuracy of the predictor, no matter the choice of the prediction model. However, there has been no extensive search for a figure of merit supporting a comparative analysis. Here, two partition policies, denoted sequential and random, are compared, and among the variable selection approaches using wrappers, forward selection is contrasted with sensitivity based pruning. Five real financial time series with trends and seasonality have been considered and multilayer perceptrons are adopted as the predictor. The obtained results indicate with high confidence that the rarely adopted random partition and the computationally intensive forward selection overcomes the contestants in the whole set of experiments.
Starting Page	4740
Ending Page	4747
File Size	537254
Page Count	8
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247129
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Input variables Data preprocessing Predictive models Forward contracts Multilayer perceptrons Artificial neural networks Machine learning Data mining Accuracy Economic forecasting
Content Type	Text
Resource Type	Article

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