Artificial bee colony-based combination approach to forecasting agricultural commodity prices

The fluctuation of agricultural commodity prices has attracted a considerable amount of attention. However, the complexity of the agricultural futures market and the variability of influencing factors makes the prediction of agricultural commodity futures prices difficult. We address the nonlinear characteristics of agricultural commodity futures price series by proposing a forecast combination approach based on a global optimization method, called the Artificial Bee Colony Algorithm (ABC), for forecasting soybean and corn futures prices. Firstly, we used three denoising techniques, namely singular spectral analysis (SSA), empirical mode decomposition (EMD), and variational mode decomposition (VMD), to filter the external noise in the original price series. Then, we generated diverse forecasting sub-models by combining denoising techniques and five popular forecasting models: autoregressive integrated moving average regression (ARIMA), support vector regression (SVR), recurrent neural network (RNN), gated recurrent neural network (GRU), and long-short term memory neural network (LSTM). Finally, we present an ABC approach for three forecast combinations: heterogeneous, semi-heterogeneous, and homogeneous combination. Experimental results indicate that the semi-heterogeneous forecast combination is superior to other combination strategies. For corn and soybean prices, ABC-based semi-heterogeneous forecast combinations have error reductions of 53.3% and 50.0% of MAPE and improvements of 32.4% and 34.5% in Dstat compared to the best single models, respectively.