NDLI: Long-term prediction of time series using NNE-based projection and OP-ELM

Content Provider	IEEE Xplore Digital Library
Author	Sorjamaa, A. Miche, Y. Weiss, R. Lendasse, A.
Copyright Year	2008
Description	Author affiliation: Adaptive Inf. Res. Centre, Helsinki Univ. of Technol., Espoo (Sorjamaa, A.; Miche, Y.; Lendasse, A.) \|\| Process Vision OY, Helsinki (Weiss, R.)
Abstract	This paper proposes a combination of methodologies based on a recent development -called Extreme Learning Machine (ELM)- decreasing drastically the training time of nonlinear models. Variable selection is beforehand performed on the original dataset, using the Partial Least Squares (PLS) and a projection based on Nonparametric Noise Estimation (NNE), to ensure proper results by the ELM method. Then, after the network is first created using the original ELM, the selection of the most relevant nodes is performed by using a Least Angle Regression (LARS) ranking of the nodes and a Leave-One-Out estimation of the performances, leading to an Optimally-Pruned ELM (OP-ELM). Finally, the prediction accuracy of the global methodology is demonstrated using the ESTSP 2008 Competition and Poland Electricity Load datasets.
Starting Page	2674
Ending Page	2680
File Size	301110
Page Count	7
File Format	PDF
ISBN	9781424418206
ISSN	10987576
DOI	10.1109/IJCNN.2008.4634173
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Input variables Neurons Noise Time series analysis Predictive models Least squares approximation
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
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