NDLI: FI-GEM networks for incomplete time-series prediction

Content Provider	IEEE Xplore Digital Library
Author	Chiewchanwattana, S. Lursinsap, C.
Copyright Year	2002
Description	Author affiliation: Dept. of Comput. Sci., Khon Kaen Univ., Thailand (Chiewchanwattana, S.)
Abstract	This paper considers the problem of incomplete time-series prediction by FI-GEM (fill-in-generalized ensemble method) networks, which has two steps. The first step is composed of several fill-in methods for preprocessing the missing value of time-series and the outcome are the complete time-series data. The second step is composed of the several individual multilayer perceptrons (MLP) whose their outputs are combined by the generalized ensemble method. There are five fill-in methods that are explored: cubic smoothing spline interpolation, and four imputation methods: EM (expectation maximization), regularized EM, average EM, average regularized EM. Mackey-Glass chaotic time-series and sunspots data are used for evaluating our approach. The experimental results show that the prediction accuracy of FI-GEM networks are much better than individual neural networks.
Sponsorship	IEEE IEEE Neural Networks Soc. (NNS) Int. Neural Network Soc
Starting Page	1757
Ending Page	1762
File Size	546694
Page Count	6
File Format	PDF
ISBN	0780372786
ISSN	10987576
DOI	10.1109/IJCNN.2002.1007784
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Smoothing methods Spline Interpolation Accuracy Intelligent networks Computer networks Mathematics Computer science Multilayer perceptrons
Content Type	Text
Resource Type	Article

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