Neural network versus classical time series forecasting models

Maria Elena Nor, Hamizah Mohd Safuan, Noorzehan Fazahiyah Md Shab, Mohd Asrul, Affendi Abdullah, Nurul Asmaa Izzati Mohamad, Muhammad Hisyam Lee

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

5 Citations (Scopus)

Abstract

Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

Original languageEnglish
Title of host publication3rd ISM International Statistical Conference 2016, ISM 2016
Subtitle of host publicationBringing Professionalism and Prestige in Statistics
EditorsShaiful Anuar Abu Bakar, Ibrahim Mohamed, Rossita Mohamad Yunus
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735415126
DOIs
Publication statusPublished - 12 May 2017
Externally publishedYes
Event3rd ISM International Statistical Conference 2016: Bringing Professionalism and Prestige in Statistics, ISM 2016 - Kuala Lumpur, Malaysia
Duration: 9 Aug 201611 Aug 2016

Publication series

NameAIP Conference Proceedings
Volume1842
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd ISM International Statistical Conference 2016: Bringing Professionalism and Prestige in Statistics, ISM 2016
Country/TerritoryMalaysia
CityKuala Lumpur
Period9/08/1611/08/16

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