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 a linear model and a non-linear model in doing the vegetable price forecasting model. The hybrid model combining seasonal autoregressive integrated moving average (SARIMA)-discrete wavelet transform (DWT)-genetic algorithm neural network (GANN), referred to SARIMA-DWT-GANN, was used to forecast monthly vegetable prices in Malaysia. The historical vegetable price data is collected from the federal agricultural marketing authority Malaysia and split into training/test sets for modeling. The performance of the models is evaluated on the accuracy metrics including mean absolute error (MAE), mean absolute percentage error, and root mean square error (RMSE). The forecasted results using the proposed hybrid model are compared to those using the single SARIMA model. In conclusion, the hybrid SARIMA-DWT-GANN model is superior to the individual model, which obtained the smaller MAE and RMSE, and got the forecast accuracy of at least 95%.
