(untitled) (American Journal of Applied Statistics and Economics, 2025)
The global wheat market is influenced by several factors, such as climate change, inflation, market fluctuations, geopolitical situations, and government policies, leading to continuous fluctuations in wheat prices. There is a need for a robust forecasting model that accurately captures seasonal variations and trends in global wheat prices to support informed decision-making. The objective of this study was to forecast the monthly global price of wheat by adopting the SARIMA model using historical data from January 1990 to October 2024. The original monthly global wheat price data was log-transformed to stabilize the variance in the data and improve forecast precision. The seasonal variations in the data were adjusted by applying decomposition and differencing before modeling. Using the ‘auto.arima’ function from the ‘forecast’ package in R 4.3.3 for Windows, SARIMA(0,1,1)(0,0,1)12 was identified as the best-fitted model for forecasting global wheat prices. Residual analysis validated the model’s accuracy by visualizing the ACF and PACF plots and applying the Ljung-Box test, confirming that the residuals were white noise. The model forecasted a steady rise in the monthly global price of wheat from 198.51 per metric ton in November 2024 to 201.36 per metric ton in October 2025, peaking at 203.62 per metric ton in August 2025. These projections could help farmers, policymakers, and other relevant stakeholders to anticipate global price fluctuations and make informed decisions amidst global uncertainties. Future research could integrate external factors, such as climate change and geopolitical events, for enhanced predictive accuracy.