Fuzzy Time Series and Artificial Neural Network: Forecasting Exportation of Natural Rubber in Malaysia

Siti Nor Nadrah Muhamad; Shafeina Hatieqa Sofean; Balkiah Moktar; Wan Nurshazelin Wan Shahidan

doi:10.24191/jcrinn.v6i1.170

Fuzzy Time Series and Artificial Neural Network: Forecasting Exportation of Natural Rubber in Malaysia

Authors

Siti Nor Nadrah Muhamad Universiti Teknologi MARA Perlis Branch, Arau Campus
Shafeina Hatieqa Sofean Universiti Teknologi MARA Perlis Branch, Arau Campus
Balkiah Moktar Universiti Teknologi MARA Perlis Branch, Arau Campus
Wan Nurshazelin Wan Shahidan Universiti Teknologi MARA, Perlis Branch, Arau Campus

DOI:

https://doi.org/10.24191/jcrinn.v6i1.170

Keywords:

fuzzy time series, artificial neural network, forecasting, Conjugate Gradient Descent

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.

Downloads

Download data is not yet available.

References

Abhishek, K., Kumar, A., Ranjan, R., & Kumar, S. (2012, July). A rainfall prediction model using

artificial neural network. Paper presented at 2012 IEEE Control and System Graduate Research Colloquium, Selangor, Malaysia.

Cai, Q., Zhang, D., Zheng, W., & Leung, S. C. (2015). A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression. Knowledge-Based Systems, 74, 61-68.

Cornish, K. (2017). Alternative natural rubber crops: Why should we care? Technology & Innovation, 18(4), 245-256.

Dani, S., & Sharma, S. (2013), Forecasting rainfall of a region by using Fuzzy Time Series, Asian Journal of Mathematics & Apllications, 2013, 1-10.

Dani, S., Khan, A.J., & Sharma, S. (2019), Forecasting average rainfall model based on Fuzzy Time Series in Chhattisgarh State, International Journal of Advanced Scientific Research & Management (IJASRM), 4(6), 225-232.

Department of Statistics Malaysia (2020), Monthly Rubber Statistics Malaysia. Retrieved March 30, 2020, from https://www.dosm.gov.my/v1/index.php?r=column/cthemeByCat&cat=73&bul_id=aGZ2Vzl1NlVQaWErYTMzSTg4K1Z3QT09&menu_id=Z0VTZGU1UHBUT1VJMFlpaXRRR0xpdz09

Huarng, K.H., Yu, T.H.K, Moutinho, L., & Wang, Y.C. (2012), Forecasting tourism demand by Fuzzy Time Series models, International Journal of Culture, Tourism & Hospitality Research, 6(4), 377-388.

Kumar, P., Kashyap, P. S., & Ali, J. (2013). Temperature forecasting using artificial neutral networks (ANN). Journal of Hill Agriculture, 4(2), 110-112.

Kumar, S., Kumar, V., & Sharma. R. K. (2015). Sugarcane yield forecasting using artificial neural network models. International Journal of Artificial Intelligence & Applications (IJAIA), 6(5), 51-68.

Md Ludin, N. H., Applanaidu, S., & Abdullah, H. (2016). An econometric analysis of natural rubber market in Malaysia. International Journal of Environmental & Agriculture Research (IJOEAR), 2(6), 29-37.

Narvekar, M., & Fargose, P. (2015). Daily weather forecasting using artificial neural network. International Journal of Computer Applications, 121(22), 9-13.

Othman, F., & Naseri, M. (2011). Reservoir inflow forecasting using artificial neural network. International Journal of Physical Sciences, 6(3), 434-440.

Sarahintu, M., & Tarmudi, Z. (2015). Forecasting tourist arrivals to Sabah using fuzzy time series, In Proceedings of the International Conference on Natural Resources, Tourism and Services Management 2015, Sabah, Malaysia, 15-17 April 2015 (pp. 481-488). Universiti Putra Malaysia.

Sy Ahmad Ubaidillah, S. H., & Sallehuddin, R. (2013). Forecasting zakat collection using artificial neural network. AIP Conference Proceedings, 1522(1), 196-204.

Warren?Thomas, E., Dolman, P. M., & Edwards, D. P. (2015). Increasing demand for natural rubber necessitates a robust sustainability initiative to mitigate impacts on tropical biodiversity. Conservation Letters, 8(4), 230-241.