Rare events forecasting using a residual-feedback GMDH neural network
Fong,Simon1; Nannan,Zhou1; Wong,Raymond K.2; Yang,Xin She3
Source PublicationProceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
AbstractThe prediction of rare events is a pressing scientific problem. Events such as extreme meteorological conditions, may aggravate human morbidity and mortality. Yet, their prediction is inherently difficult as, by definition, these events are characterised by low occurrence, high sampling variation, and uncertainty. For example, earthquakes have a high magnitude variation and are irregular. In the past, many attempts have been made to predict rare events using linear time series forecasting algorithms, but these algorithms have failed to capture the surprise events. This study proposes a novel strategy that extends existing GMDH or polynomial neural network techniques. The new strategy, called residualfeedback, retains and reuses past prediction errors as part of the multivariate sample data that provides relevant multivariate inputs to the GMDH or polynomial neural networks. This is important because the strength of GMDH, like any neural network, is in predicting outcomes from multivariate data, and it is very noise-tolerant. GMDH is a well-known ensemble type of prediction method that is capable of modelling highly non-linear relations. It achieves optimal accuracy by testing all possible structures of polynomial forecasting models. The performance results of the GMDH alone, and the extended GMDH with residual-feedback are compared for two case studies, namely global earthquake prediction and precipitation forecast by ground ozone information. The results show that GMDH with residualfeedback always yields the lowest error. © 2012 IEEE.
KeywordData Pre-processing Earthquake prediction GMDH Ground ozone Neural network Time series forecasting
URLView the original
Fulltext Access
Citation statistics
Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Corresponding AuthorFong,Simon
Affiliation1.Department of Computer and Information Science,University of Macau,Macao
2.National ICT Australia,University of New South Wales,Sydney, NSW 2052,Australia
3.School of Science and Technology,Middlesex University,London NW4 4BT,United Kingdom
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Fong,Simon,Nannan,Zhou,Wong,Raymond K.,et al. Rare events forecasting using a residual-feedback GMDH neural network[C],2012:464-473.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Fong,Simon]'s Articles
[Nannan,Zhou]'s Articles
[Wong,Raymond K.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Fong,Simon]'s Articles
[Nannan,Zhou]'s Articles
[Wong,Raymond K.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Fong,Simon]'s Articles
[Nannan,Zhou]'s Articles
[Wong,Raymond K.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.