@inproceedings{193adc05f19d4f43bdfe4f14e9d8affe,
title = "Forecasting Macao GDP using different artificial neural networks",
abstract = "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.",
keywords = "Artificial neural network, Back-propagation, Elman, Forecasting GDP, Radial basic function",
author = "Xu Yang and Zheqi Zhang and Laurie Cuthbert and Yapeng Wang",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; International Conference on Information Science and Applications, ICISA 2018 ; Conference date: 25-06-2018 Through 27-06-2018",
year = "2019",
doi = "10.1007/978-981-13-1056-0_44",
language = "English",
isbn = "9789811310553",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "431--442",
editor = "Kim, {Kuinam J.} and Kim, {Kuinam J.} and Nakhoon Baek",
booktitle = "Information Science and Applications 2018 - ICISA 2018",
address = "Germany",
}