Forecasting Monthly Prices of Selected Agricultural Commodities in The Philippines Using ARIMA Model

Abstract

Prices of commodities affected both producers and consumers thus, determining its future value is relevant for future decision-making. This study aims to guide the policymakers in creating guidelines for the benefit of the producers and consumers of agricultural commodities like sitao, eggplant, tomato, whole chicken, pork ham, and pork liempo. The researchers analyzed the data behavior of the selected commodities for the years 2013-2022 which all be observed to have an upward trend with fluctuations. These fluctuations are found to be connected to different factors such as seasonality of production, surplus of volume, pest & diseases, typhoon devastation, and importation, among others. After the analysis of price behavior, the researcher then, forecasted the price of this agricultural produce using the ARIMA technique. The data was first tested for its stationary through Augmented Dicker Fuller (ADF) Test, which resulted in the first differencing. The results of the ARIMA technique revealed that ARIMA (2,1,2), ARIMA (8,1,3), ARIMA (9,1,3), ARIMA (67,1,29), ARIMA (1,1,35) ARIMA (3,1,2), ARIMA (1,13), ARIMA (3,1,6), ARIMA (3,1,2), and ARIMA (3,2,5) for the whole chicken, pork ham, pork belly, beef brisket, chicken egg, sitao, eggplant, tomato, carrot, and cabbage respectively, are the best-fit models to forecast the next five years (2023-2027) prices of the commodities