TY - GEN
T1 - Neural network versus classical time series forecasting models
AU - Nor, Maria Elena
AU - Safuan, Hamizah Mohd
AU - Shab, Noorzehan Fazahiyah Md
AU - Asrul, Mohd
AU - Abdullah, Affendi
AU - Mohamad, Nurul Asmaa Izzati
AU - Lee, Muhammad Hisyam
N1 - Publisher Copyright:
© 2017 Author(s).
PY - 2017/5/12
Y1 - 2017/5/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85019748731&partnerID=8YFLogxK
U2 - 10.1063/1.4982865
DO - 10.1063/1.4982865
M3 - Conference contribution
AN - SCOPUS:85019748731
T3 - AIP Conference Proceedings
BT - 3rd ISM International Statistical Conference 2016, ISM 2016
A2 - Abu Bakar, Shaiful Anuar
A2 - Mohamed, Ibrahim
A2 - Yunus, Rossita Mohamad
PB - American Institute of Physics Inc.
T2 - 3rd ISM International Statistical Conference 2016: Bringing Professionalism and Prestige in Statistics, ISM 2016
Y2 - 9 August 2016 through 11 August 2016
ER -