Abstract
Accurately predicting agricultural commodity prices is challenging due to their unpredictable and complex nature. Existing models often fail to capture nonlinear and nonstationary patterns in price data, resulting in less accurate forecasts. To tackle these challenges, we present a novel hybrid VMD-LSTM model that synergistically combines genetic algorithm (GA), variational mode decomposition (VMD), and long short-term memory (LSTM), leading to better prediction accuracy. The proposed model utilizes GA-optimized VMD, which decomposes a price series into intrinsic mode functions (IMFs) with a unique property of sparsity leading to faster convergence. Then, these IMFs are individually modelled and forecasted using GA-optimized LSTM models. Finally, the forecasts of all IMFs are ensembled to provide an output for the actual price series. VMD-LSTM is evaluated against individual LSTM and decomposition-based models (EMD-LSTM, EEMD-LSTM, CEEMDAN-LSTM) using monthly price data for maize, palm oil, and soybean oil. Performance is assessed through root mean square error (RMSE), mean absolute percentage error (MAPE), and directional prediction statistics ([Formula: see text]). VMD-LSTM reduces RMSE by 56.93%, 21.83%, and 27.00% and MAPE by 44%, 21.67%, and 25.85% for maize, palm oil, and soybean oil, respectively, compared to the next best CEEMDAN-LSTM. TOPSIS and Diebold-Mariano test also confirm the better prediction accuracy of VMD-LSTM. The proposed model can be a better tool for agricultural price forecasting, supporting decision-making for farmers, traders, and policymakers.