Price Forecasting Analysis of Red Chilli Using Seasonal Regression and Quadratic Trend Models

Abstract

This study aims to compare the effectiveness of the seasonal regression model and the quadratic trend model with seasonal indices in predicting the price of red chilli in Johor, Malaysia. Historical price data from 2018 until 2022 of red chilli in Johor, Malaysia, were collected and analyzed using the seasonal regression model and the quadratic trend model with seasonal indices. The forecasting accuracy and predictive performance of each model were evaluated and compared. The results indicated that the seasonal regression model outperforms the quadratic trend model with seasonal indices in predicting the price of red chilli. This study contributes to the field of agricultural forecasting by comparing different models for price prediction and highlighting the importance of considering multiple crops in the analysis. The findings of this study have practical implications for policymakers, stakeholders, and researchers involved in agricultural planning and food security. The identified seasonal regression model can serve as a valuable tool for predicting the price of red chilli, enabling more informed decision-making in crop production and market interventions.