Forecasting Indonesian Monthly Rice Prices at Milling Level Using Google Trends and Official Statistics Data

Abstract

Hunger is a very complex social issue to address. Alleviating hunger is closely related to achieving food security, which is a goal in realizing the second Sustainable Development Goals (SDGs), zero hunger. The most frequently consumed food commodity by the Indonesian population is rice, which has fluctuating prices in the market. Therefore, price forecasting is necessary so that the government can take preventive measures against rice price increases at certain times. Research on rice price forecasting using big data from Google Trends is still very rare in Indonesia, even though Google Trends has great potential to reflect the public's search popularity for certain keywords. Therefore, this study aims to forecast the monthly medium rice price in Indonesia at the milling level using exogenous variables of dried milled grain prices and the popularity index of related keywords on Google Trends. The forecasting is conducted using Seasonal Autoregressive Integrated Moving Average (SARIMA), SARIMA with Exogenous Variables (SARIMAX), and Extreme Gradient Boosting (XGBoost) models. The SARIMAX model has the best performance in forecasting rice prices, with a Root Mean Squared Error (RMSE) of 941.6933, Mean Absolute Error (MAE) of 817.9021, and Mean Absolute Percentage Error (MAPE) of 0.0620.