Neural network versus classical time series forecasting models

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...

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Bibliographic Details
Main Authors: Nor, Maria Elena, Safuan, Hamizah Mohd, Shab, Noorzehan Fazahiyah Md, Asrul, Mohd, Abdullah, Affendi, Mohamad, Nurul Asmaa Izzati, Lee, Muhammad Hisyam
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Autoregressive models
Forecasting
Forecasting techniques
Gold
Neural networks
Preprocessing
Time series
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Summary: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.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.4982865