A comparative study between univariate and bivariate time series models for crude palm oil industry in peninsular Malaysia / Pauline Jin Wee Mah and Nur Nadhirah Nanyan

Wee Mah, Pauline Jin and Nanyan, Nur Nadhirah (2020) A comparative study between univariate and bivariate time series models for crude palm oil industry in peninsular Malaysia / Pauline Jin Wee Mah and Nur Nadhirah Nanyan. Malaysian Journal of Computing (MJoC), 5 (1). pp. 374-389. ISSN (eISSN): 2600-8238

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Abstract

The main purpose of this study is to compare the performances of univariate and bivariate models on four-time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA (1,1,0) while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA (0,1,0) appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA (0, 0.08903, 0) emerged the best model based on the RMSE value. When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

Metadata

Item Type: Article
Creators:
Creators
Email
Wee Mah, Pauline Jin
pauli874@fskm.uitm.edu.my
Nanyan, Nur Nadhirah
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Programming. Rule-based programming. Backtrack programming
Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Operating systems (Computers)
Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > System design
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences
Journal or Publication Title: Malaysian Journal of Computing (MJoC)
UiTM Journal Collections: UiTM Journal > Malaysian Journal of Computing (MJoC)
ISSN: (eISSN): 2600-8238
Volume: 5
Number: 1
Page Range: pp. 374-389
Official URL: https://mjoc.uitm.edu.my
Item ID: 48081
Uncontrolled Keywords: Crude palm oil, ARIMA, ARFIMA
URI: https://ir.uitm.edu.my/id/eprint/48081

ID Number

48081

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