Hybrid approach for vegetable price forecasting in electronic commerce platform

Abstract

The significance of the agriculture sector in Malaysia is often overlooked, and there is a notable deficiency in the advancement of digitalization within the country's agricultural domain. The integration of a price forecasting model in the platform enables the relevant parties, including farmers, to make informed decisions and plan their crop selection based on projected future prices. In this research, the authors proposed the hybrid approach with the combination of linear model and non-linear model in doing the vegetable price forecasting model. The hybrid SARIMA-DWT-GANN model is utilized to forecast the monthly vegetable prices in Malaysia. The historical vegetable price data is collected from the FAMA Malaysia and split into training/test set for modelling. The performance of the models is evaluated on the accuracy metrics including MAE, MAPE, and RMSE. The forecasted results using the proposed hybrid model are compared to that using the single SARIMA model. In conclusion, the hybrid SARIMA-DWT-GANN model is superior to the individual model, which obtained the smaller MAE, RMSE, and got the forecast accuracy of at least 95%.