A modified unbiased GM(1,1) prediction model based EEMD

Haixu Jiang, Ke Zhang, Jingyu Wang, Tianshe Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

The grey model GM(1,1) is widely applied in prediction problems, but the applicable range and the prediction precision of classical GM(1,1) is limited by the background value. In order to improve the applicability of this model, a modified unbiased grey prediction model GM(1,1), which is based on ensemble empirical mode decomposition (EEMD) is put forward. The original data is decomposed into a finite number of intrinsic mode functions (IMFs) by EEMD, and a conversion formula is used to improve the exponential smoothing of every IMF, then using each processed IMF components as input data of Unbiased GM(1,1), we accumulate all results and get the final predict result of the original data. An example is shown that the modified Unbiased GM(1,1) prediction model based on EEMD solved the predicting singular points problem of classical GM(1,1) model and improved the prediction precision.

Original languageEnglish
Title of host publicationCGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2027-2031
Number of pages5
ISBN (Electronic)9781467383189
DOIs
StatePublished - 20 Jan 2017
Event7th IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2016 - Nanjing, Jiangsu, China
Duration: 12 Aug 201614 Aug 2016

Publication series

NameCGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference

Conference

Conference7th IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2016
Country/TerritoryChina
CityNanjing, Jiangsu
Period12/08/1614/08/16

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