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A forecasting method based on extrema mean empirical mode decomposition and wavelet neural network
Pan J.; Zheng X.; Yang L.; Wang Y.; Yuan H.; Tang Y.Y.
2015
Conference Name2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)
Source PublicationProceedings - 2015 IEEE 2nd International Conference on Cybernetics, CYBCONF 2015
Pages377-381
Conference Date24-26 June 2015
Conference PlaceGdynia, Poland
CountryPoland
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

Time series forecasting is a widely and important research area in signal processing and machine learning. With the development of the artificial intelligence (AI), more and more AI technologies are used in time series forecasting. Multi-layer network structure has been widely used for forecasting problems. In this paper, based on a data-driven and adaptive method, extrema mean empirical mode decomposition, we proposed a decomposition-forecasting-ensemble approach to time series forecasting. Experimental result shows the prediction result by proposed models are better than original signal and EMD based models.

KeywordEmpirical Mode Decomposition Forecasting Wavelet Neural Network
DOIhttps://doi.org/10.1109/CYBConf.2015.7175963
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Cybernetics
WOS IDWOS:000373207200066
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Pan J.,Zheng X.,Yang L.,et al. A forecasting method based on extrema mean empirical mode decomposition and wavelet neural network[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2015:377-381.
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