Performance of Chili Price Forecasting Models in Johor: A Comparative Study

Abstract

A substantial portion of household income in Malaysia is allocated to food expenditures, and chili is a staple ingredient in Malaysian cuisine. Fluctuations in chili prices directly affect the cost of living for individuals and families, impacting their purchasing power and overall well-being. Forecasting chili prices helps in effective supply chain management. Producers, distributors, and retailers can plan and adjust their operations based on anticipated price trends. This, in turn, contributes to the country's efficiency and stability of the chili supply chain. This study emphasises the importance of comparing various forecasting models to identify the most accurate predictors of chili prices. The goal is to develop a model that can contribute to more informed decision-making in crop production and market interventions, ultimately promoting stability in the chili industry and ensuring sustainable practices. Statistical models, time series forecasting models and machine learning models which include multiple linear regression (MLR), Auto Regressive Integrated Moving Average with exogenous inputs (ARIMAX), and machine learning models that consist of Support Vector Regression (SVR) were tested and compared using ex-farm prices in Johor with the duration of 5 years, starting from 2018 to 2022. This study reveals that SVR under machine learning algorithms performed best as the forecasted model followed by ARIMAX and MLR. However, ARIMAX models, an extension of the ARIMA model, effectively capture and predict patterns by incorporating significant exogenous variables. Overall, the results show that the price of fertilisers, Movement Control Order (PKP) season and chili production significantly affect the prices of chilies.