Home⇛The Advanced Social Science in Research Journal⇛vol. 1 no. 5 (2026)

Seasonal ARIMA Analysis and Forecasting of Corn Production in South Cotabato, Philippines

Karl Evan Pama | Darryl Jean Gevero

Discipline: Agriculture

 

Abstract:

In this research, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model is applied using stochastic time-series analysis to represent, model, and predict the quarterly corn production data in South Cotabato, Philippines. Past corn production data were obtained for the period between 1987 and 2019 for model building while years 2020 to 2025 were excluded for validation purposes, followed by generating forecast values for years 2026 to 2030. The BoxJenkins procedure was employed in analyzing time-series data, which included exploratory analysis, stationarity test, identification, estimation, diagnostics, and forecasting procedures. It was found that there is a strong seasonal quarter effect in the quarterly production of corn with higher production level in the third and fourth quarters but lower production levels in the first and second quarters. Based on diagnostic tests, the best-fitting model for quarterly corn production was found to be SARIMA(3,1,0)(0,0,1)[4]. The model demonstrated moderate forecasting accuracy, as errors were mainly due to past variations and structural breaks during early periods of the dataset. Predictions for years 2026 to 2030 showed that the model produced stable seasonal cycles with similar production levels and no clear trend indicating an asymptotic level.



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