Robustness analysis of artificial neural networks and support vector machine in making prediction

Saiful Anwar, Rifki Ismal

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

5 Citations (Scopus)

Abstract

This This study aims to investigate the robustness of prediction model by comparing artificial neural networks (ANNs), and support vector machine (SVMs) model. The study employs ten years monthly data of six types of macroeconomic variables as independent variables and the average rate of return of one-month time deposit of Indonesian Islamic banks (RR) as dependent variable. Finally, the performance is evaluated through graph analysis, statistical parameters and accuracy rate measurement. This research found that ANNs outperforms SVMs empirically resulted from the training process and overall data prediction. This is indicating that ANNs model is better in the context of capturing all data pattern and explaining the volatility of RR.

Original languageEnglish
Title of host publicationProceedings - 9th IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2011
Pages256-261
Number of pages6
DOIs
Publication statusPublished - 18 Aug 2011
Event9th IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2011 - Busan, Korea, Republic of
Duration: 26 May 201128 May 2011

Publication series

NameProceedings - 9th IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2011

Conference

Conference9th IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2011
CountryKorea, Republic of
CityBusan
Period26/05/1128/05/11

Keywords

  • Artificial neural networks
  • Islamic bank
  • Rate of return
  • Support vector machine

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