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An ensemble of single multiplicative neuron models for probabilistic prediction

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Publication date13/02/2017
Host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781509042401
<mark>Original language</mark>English
Event2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece
Duration: 6/12/20169/12/2016

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Country/TerritoryGreece
CityAthens
Period6/12/169/12/16

Publication series

Name2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Country/TerritoryGreece
CityAthens
Period6/12/169/12/16

Abstract

Inference systems basically aim to provide and present the knowledge (outputs) that decision-makers can take advantage of in their decision-making process. Nowadays one of the most commonly used inference systems for time series prediction is the computational inference system based on artificial neural networks. Although they have the ability of handling uncertainties and are capable of solving real life problems, neural networks have interpretability issues with regard to their outputs. For example, the outputs of neural networks that are difficult to interpret compared to statistical inference systems' outputs that involve a confidence interval and probabilities about possible values of predictions on top of the point estimations. In this study, an ensemble of single multiplicative neuron models based on bootstrap technique has been proposed to get probabilistic predictions. The main difference of the proposed ensemble model compared to conventional neural network models is that it is capable of getting a bootstrap confidence interval and probabilities of predictions. The performance of the proposed model is demonstrated on different time series. The obtained results show that the proposed ensemble model has a superior prediction performance in addition to having outputs that are more interpretable and applicable to probabilistic evaluations than conventional neural networks.