Optimal forecast combination based on PSO-CS approach for daily agricultural future prices forecasting

Abstract

Forecasting agricultural commodity prices accurately is a challenging task due to the complexity of the trading market and the variability of influencing factors. Many studies have proven that forecast combination is an effective strategy for improving forecast performance relative to individual forecasting. In the field of forecast combination, how to determine the reasonable weights for combination is still an open question. This study proposed an optimal forecast combination framework for agricultural commodity prices forecasting, which integrates the decomposition–reconstruction–ensemble methodology with an improved nature-inspired global optimization algorithm. The update mechanism of particle swarm optimization (PSO) is introduced to improve cuckoo search (CS), in order to reduce the searching blindness in the huge exploration space. The framework consists of four steps. First, data decomposition using empirical wavelet transform (EWT), singular spectral analysis (SSA), and variational mode decomposition (VMD); Second, component reconstruction via a modified reconstruction approach based on the largest comprehensive grey correlation degree clustering (CGCD); Third, individual forecasting using autoregressive integrated moving average regression (ARIMA), exponential smoothing (ETS), back propagation neural network (BPNN) and extreme learning machine (ELM); Fourth, forecast combination via PSO-CS weight assignment method. Using corn and wheat future prices as research samples, the experimental results demonstrated that: (a) the PSO-CS weight assignment approach is superior to other combination approaches in most cases; (b) the CGCD approach can effectively reduce the computational cost of forecasting and improve the prediction performance; (c) the Full-PSO-CS model provides the most accurate forecast due to the diversity of individual forecasts, it reduces MAPE by 43.66% and improves directional accuracy by 30.80% on average compared with the best single model.