A Comparative Study Between Univariate and Bivariate Time Series Models for Crude Palm Oil Industry in Peninsular Malaysia

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.

Composite Model Analysis in Forecasting the Malaysian Imports

“…The forecasting results for imports showed that the ARIMA (2,1,2) model had the best fit. the ARIMA models, ARFIMA models and autoregressive (ARAR) algorithm were used for in order to forecast Malaysia imports (Wee Mah and Nanyan, 2020). Merous and Asfaria (2017), analysed the imports of Malaysia for goods and used multiple regression model, Input-output model, composite model and ARIMA model.…”

Forecasting Electricity Consumption Using Exponential Smoothing Methods

“…While Akdi et al (2020) forecasted daily electricity consumption in Turkey using ARIMA and harmonic regression models for the amounts consumed each day of electricity in Turkey between 2012 and 2016. Researched by Wee Mah and Nanyan (2020) examined results of a few models using four-time exponential series techniques used in Peninsular Malaysia's crude palm oil business. The results indicated that the bivariate models outperformed the univariate models for the production and export of crude palm oil based on the forecast accuracy criteria that were used.…”