Forecasting Macao GDP using different artificial neural networks

Xu Yang, Zheqi Zhang, Laurie Cuthbert, Yapeng Wang

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

1 Citation (Scopus)


The objective of this paper is to forecast quarterly GDP in Macao using different neural network models. It is a challenge task due to the scarcity of determinant economic indicators and the scarcity of economic data. We compared the forecast errors of three different neural network models including Back Propagation (BP), Elman and Radial Basis Function (RBF). Elman has never been used in the GDP forecasting in literature, however in our results, Elman has the least forecasting error due to its recurrent network topology which can remember the history economic data.

Original languageEnglish
Title of host publicationInformation Science and Applications 2018 - ICISA 2018
EditorsKuinam J. Kim, Kuinam J. Kim, Nakhoon Baek
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9789811310553
Publication statusPublished - 2019
EventInternational Conference on Information Science and Applications, ICISA 2018 - Kowloon, Hong Kong
Duration: 25 Jun 201827 Jun 2018

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


ConferenceInternational Conference on Information Science and Applications, ICISA 2018
Country/TerritoryHong Kong


  • Artificial neural network
  • Back-propagation
  • Elman
  • Forecasting GDP
  • Radial basic function


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