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Neural network versus classical time series forecasting models

Nor, Maria Elena and Mohd Safuan, Hamizah and Md Shab, Noorzehan Fazahiyah and Abdullah, Mohd Asrul Affendi and Mohamad, Nurul Asmaa Izzati and Lee, Muhammad Hisyam (2017) Neural network versus classical time series forecasting models. In: AIP Conference Proceedings.

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > T Technology (General)
Depositing User: Mr Abdul Rahim Mat Radzuan
Date Deposited: 17 Mar 2020 02:26
Last Modified: 17 Mar 2020 02:26
URI: http://eprints.uthm.edu.my/id/eprint/12873
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