Price instability is a paramount concern since commodity prices are associated with the livelihood and the economy of a nation as a whole; any extraordinary price fluctuation in the futures market shows that forecasts in commodities is an essential venture. The difficulties in predicting commodity prices are due to the unpredictability of the world's financial issues, fiscal dispensation, the speculative market's exacerbation, and several other elements. This study aims to model and forecast the market price of commodity futures. We applied decomposition techniques, empirical mode decomposition (EMD), and variational mode decomposition (VMD) to three commodities: corn, crude oil, and gold over the commodity spot market prices. We used the Granger causality test to establish mutual relationships among the three commodity futures prices. Three commodity price data with different periods were decomposed into several intrinsic modes. Using three forecasting performance evaluation criteria, statistical measures such as mean absolute error (MAE), root mean square error (RMSE), and mean percentage error (MAPE) to compare the capabilities of the suggested models. We also introduced Diebold Mariano (DM) test in selecting the optimal models for each commodity, since MAE, RMSE and MAPE have some shortcomings. We found that the combined models outperformed the individual back propagation neural network (BPNN) and autoregressive integrated moving average (ARIMA) models in forecasting corn and crude oil futures prices series, while BPNN emerged as the optimal model for predicting gold futures prices series. Variational mode decomposition emerged as the ideal data pre-treatment method and contributed to enhancing the predicting ability of the BPNN and the ARIMA models. The empirical results showed that models combined with decomposition methods predict commodity futures prices accurately and can easily capture the volatility in commodity futures prices.