Forecasting Models Based on Data Analytics for Predicting Rice Price Volatility: A Case Study of the Sri Lankan Rice Market

Abstract

Paddy rice is a staple food that is common among the Sri Lankan populace. However, the frequent price variation of rice has negatively impacted the Sri Lankan economy. This is due to the Sri Lankan rice market lacking the mechanisms to evaluate and predict future rice price variations, often leaving domestic traders and consumers affected by sudden price spikes. This study identifies the quantifiable economic factors that affect the sudden rice price variations and presents a viable mechanism for forecasting Domestic Rice Price (DRP). In addition, it establishes three different regression models to emphasise the relationship of DRP in Sri Lanka with three economic factors: International Rice Price (IRP), International Crude Oil Price (ICOP), and USD Exchange Rate. Further, a time series model is formulated to forecast future variations in DRP while advancing factors that have a significant, but negative, correlative impact on the DRP. The results presented in this study show that the models proposed can be used by relevant food authorities to predict sudden hikes and dips in DRP, allowing them to establish a robust price control system.