Rare Events Forecasting Using a Residual-Feedback GMDH Neural Network
Simon Fong1; Zhou Nannan1; Raymond K. Wong2; Xin-She Yang3
Conference Name2012 IEEE 12th International Conference on Data Mining Workshops
Source PublicationProceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
Conference Date10-13 Dec. 2012
Conference PlaceBrussels, Belgium
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA

The 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. 

KeywordData Pre-processing Earthquake Prediction Gmdh Ground Ozone Neural Network Time Series Forecasting
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS IDWOS:000320946500061
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Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
Affiliation1.Department of Computer and Information Science, University of Macau, Macau SAR
2.National ICT Australia and University of New South Wales, NSW 2052 Sydney, Australia
3.School of Science and Technology Middlesex University, London NW4 4BT, UK
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Simon Fong,Zhou Nannan,Raymond K. Wong,et al. Rare Events Forecasting Using a Residual-Feedback GMDH Neural Network[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2012:464-473.
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