Forecasting Malaysian foreign exchange rate using artificial neural network and ARIMA time series

Munira Ismail, Nurul Zafirah Jubley, Zalina Mohd Ali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In view of today's global economy, countries around the world are getting more connected and linked to each other via trade, technically known as import and export activities. To get ahead in the world economy, a country must actively participate in these activities. Otherwise, a country which adopt a closed door policy will be left behind due to this intra connectivity. To allow a monetary transaction to take place during a trading process in a systematic and coherent manner, exchange rate plays a prominent role. However, due to the uncertainty and volatility in the world's economy, the participants of such trading activities are vulnerable and exposed to risk. Therefore, the ability to accurately forecast the exchange rate offers a solution to mitigate this risk. The main purpose of this paper is to forecast the exchange rate for US Dollar expressed in terms of Malaysian Ringgit. The exchange rate forecasting is conducted by using two methods; Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) time series. Feed forward neural network has been chosen as neural network's method to forecast the exchange rate because this method has been proven to be inherently stable. On the other hand, ARIMA (0, 1, 1) is chosen as the best model for the time series based on Box Jenkins approach. After comparing the forecasting method using ANN and ARIMA (0, 1, 1) time series, we find that feed forward neural network exhibit a smaller mean square error and root mean square error as compared to ARIMA (0, 1, 1). This result suggest that in this research, ANN approach using the feed forward neural network is a more suitable forecasting method to predict the exchange rate for US Dollar expressed in terms of Malaysian Ringgit compared to ARIMA (0,1,1) time series model.

Original languageEnglish
Title of host publicationProceeding of the International Conference on Mathematics, Engineering and Industrial Applications 2018, ICoMEIA 2018
EditorsShazalina Mat Zin, Nur' Afifah Rusdi, Khairul Anwar Bin Mohamad Khazali, Nooraihan Abdullah, Nurshazneem Roslan, Noor Alia Md Zain, Rasyida Md Saad, Nornadia Mohd Yazid
PublisherAmerican Institute of Physics Inc.
Volume2013
ISBN (Print)9780735417298
DOIs
Publication statusPublished - 2 Oct 2018
EventInternational Conference on Mathematics, Engineering and Industrial Applications 2018, ICoMEIA 2018 - Kuala Lumpur, Malaysia
Duration: 24 Jul 201826 Jul 2018

Other

OtherInternational Conference on Mathematics, Engineering and Industrial Applications 2018, ICoMEIA 2018
CountryMalaysia
CityKuala Lumpur
Period24/7/1826/7/18

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forecasting
economy
root-mean-square errors
volatility
boxes

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Ismail, M., Jubley, N. Z., & Mohd Ali, Z. (2018). Forecasting Malaysian foreign exchange rate using artificial neural network and ARIMA time series. In S. M. Zin, N. A. Rusdi, K. A. B. M. Khazali, N. Abdullah, N. Roslan, N. A. M. Zain, R. M. Saad, ... N. M. Yazid (Eds.), Proceeding of the International Conference on Mathematics, Engineering and Industrial Applications 2018, ICoMEIA 2018 (Vol. 2013). [020022] American Institute of Physics Inc.. https://doi.org/10.1063/1.5054221

Forecasting Malaysian foreign exchange rate using artificial neural network and ARIMA time series. / Ismail, Munira; Jubley, Nurul Zafirah; Mohd Ali, Zalina.

Proceeding of the International Conference on Mathematics, Engineering and Industrial Applications 2018, ICoMEIA 2018. ed. / Shazalina Mat Zin; Nur' Afifah Rusdi; Khairul Anwar Bin Mohamad Khazali; Nooraihan Abdullah; Nurshazneem Roslan; Noor Alia Md Zain; Rasyida Md Saad; Nornadia Mohd Yazid. Vol. 2013 American Institute of Physics Inc., 2018. 020022.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ismail, M, Jubley, NZ & Mohd Ali, Z 2018, Forecasting Malaysian foreign exchange rate using artificial neural network and ARIMA time series. in SM Zin, NA Rusdi, KABM Khazali, N Abdullah, N Roslan, NAM Zain, RM Saad & NM Yazid (eds), Proceeding of the International Conference on Mathematics, Engineering and Industrial Applications 2018, ICoMEIA 2018. vol. 2013, 020022, American Institute of Physics Inc., International Conference on Mathematics, Engineering and Industrial Applications 2018, ICoMEIA 2018, Kuala Lumpur, Malaysia, 24/7/18. https://doi.org/10.1063/1.5054221
Ismail M, Jubley NZ, Mohd Ali Z. Forecasting Malaysian foreign exchange rate using artificial neural network and ARIMA time series. In Zin SM, Rusdi NA, Khazali KABM, Abdullah N, Roslan N, Zain NAM, Saad RM, Yazid NM, editors, Proceeding of the International Conference on Mathematics, Engineering and Industrial Applications 2018, ICoMEIA 2018. Vol. 2013. American Institute of Physics Inc. 2018. 020022 https://doi.org/10.1063/1.5054221
Ismail, Munira ; Jubley, Nurul Zafirah ; Mohd Ali, Zalina. / Forecasting Malaysian foreign exchange rate using artificial neural network and ARIMA time series. Proceeding of the International Conference on Mathematics, Engineering and Industrial Applications 2018, ICoMEIA 2018. editor / Shazalina Mat Zin ; Nur' Afifah Rusdi ; Khairul Anwar Bin Mohamad Khazali ; Nooraihan Abdullah ; Nurshazneem Roslan ; Noor Alia Md Zain ; Rasyida Md Saad ; Nornadia Mohd Yazid. Vol. 2013 American Institute of Physics Inc., 2018.
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