Fuzzy Time Series and Artificial Neural Network: Forecasting Exportation of Natural Rubber in Malaysia. Journal of Computing Research and Innovation, 6 (1). pp. 22-30. ISSN 2600-8793 (2021)
Abstract
Natural rubber is one of the most important crops in Malaysia alongside palm oil, cocoa, paddy, and pineapple. Being a tropical country, Malaysia is one of the top five exporters and producers of rubber in the world. The purpose of this study is to find the forecasted value of the actual data of the number of exportations of natural rubber by using Fuzzy Time Series and Artificial Neural Network. This study is also conducted to determine the best model by making comparison between Fuzzy Time Series and Artificial Neural Network. Fuzzy Time Series has allowed to overcome a downside where the classical time series method cannot deal with forecasting problem in which values of time series are linguistic terms represented by fuzzy sets. Artificial Neural Network was introduced as one of the systematic tools of modelling which has been forecasting for about 20 years ago. The error measure that was used in this study to make comparisons were Mean Square Error, Root Mean Square Error and Mean Absolute Percentage Error. The results of this study showed that the fuzzy time series method has the smallest error value compared to artificial neural network which means it was more accurate compared to artificial neural network in forecasting exportation of natural rubber in Malaysia.
| Item Type: | Article |
|---|---|
| Keywords: | Fuzzy Time Series, Artificial Neural Network, exportations of natural rubber, Mean Square Error, Root Mean Square Error, Mean Absolute Percentage Error |
| Taxonomy: | By Subject > Computer & Mathematical Sciences > Mathematics By Subject > Computer & Mathematical Sciences > Computer Technology and Networking |
| Local Content Hub: | Subjects > Computer and Mathematical Sciences |
| Depositing User: | Eza Eliana Abdul Wahid |
| Date Deposited: | 01 Aug 2021 14:47 |
| Last Modified: | 01 Aug 2021 14:47 |
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