Autoregressive Modelling with Seasonal Variations for Malaysian Crude Palm Oil Price Forecasting

Abstract

Crude palm oil (CPO) plays a vital role in Malaysia’s economy, yet its price dynamics remain highly volatile due to global market fluctuations, policy changes, and demand uncertainties. Reliable short-term forecasting is therefore essential for industry stakeholders and policymakers. This study employs Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) modelling to analyze and forecast monthly Malaysian CPO prices from 2015 to 2024. Preliminary seasonality diagnostics using STL decomposition indicated weak and statistically insignificant seasonal patterns, with a seasonal strength of 0.2046. Consequently, seasonal differencing was unnecessary, and first-order non-seasonal differencing was sufficient to achieve stationarity. Model identification and estimation were performed using an in-sample dataset (2015–2022), while model validation was conducted using out-of-sample data (2023–2024). A manual grid search across ARIMA candidates identified ARIMA(3,1,3) as the optimal model based on the Corrected Akaike Information Criterion (AICc). Residual diagnostics confirmed that the model errors behaved as white noise with no remaining autocorrelation. Out-of-sample testing further demonstrated that ARIMA(3,1,3) produced satisfactory predictive accuracy across RMSE, MAE, and MAPE metrics. Overall, the findings indicate that non-seasonal ARIMA models are sufficient for CPO price forecasting and that ARIMA(3,1,3) provides a reliable framework for short-term prediction and decision-making.