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Fuzzy Time Series and Artificial Neural Network: Forecasting Exportation of Natural Rubber in Malaysia
- Source :
- Journal of Computing Research and Innovation, Vol 6, Iss 1, Pp 22-30 (2021)
- Publication Year :
- 2021
- Publisher :
- UiTM Press, Universiti Teknologi MARA, 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.
- Subjects :
- Measure (data warehouse)
Artificial neural network
Mean squared error
Series (mathematics)
lcsh:T
Fuzzy set
forecasting
General Medicine
lcsh:Technology
Fuzzy logic
Mean absolute percentage error
Natural rubber
visual_art
lcsh:Technology (General)
Statistics
visual_art.visual_art_medium
lcsh:T1-995
fuzzy time series
lcsh:Probabilities. Mathematical statistics
lcsh:QA273-280
artificial neural network
conjugate gradient descent
Mathematics
Subjects
Details
- ISSN :
- 26008793
- Volume :
- 6
- Database :
- OpenAIRE
- Journal :
- Journal of Computing Research and Innovation
- Accession number :
- edsair.doi.dedup.....cd9d4ad1cc4b82d436936250023bfc5b