Livestock Product Price Forecasting Method Based on Heterogeneous GRU Neural Network and Energy Decomposition

Abstract

The characteristics exhibited by livestock product price fluctuation should be characterized, and the trend of price fluctuation should be forecasted in times, which are critical to developing the animal husbandry market. As reported from existing studies, the trend of price fluctuation is difficult to accurately forecast due to the multi-modality of the factors of the price fluctuation of livestock products. To address the problems, a novel price forecasting method was proposed by complying with GRU neural network and the principle of energy decomposition. First, to acquire the price fluctuation information at different frequencies, this study proposed a variation mode decomposition method based on actual signal energy (AE-VMD) and a multi-scale adaptive Lempel-Ziv complexity calculation method (MA-LZ). Second, to preserve the information of time series and multimodal data, this study developed a heterogeneous GRU neural network (AH-GRU) in accordance with attention mechanism. Lastly, the effect of static information (non-time series, including growth period, origin, longitude and latitude) on price fluctuations was introduced in the forecasting initially, and the final forecasting result was outputted via the dense layer. As indicated from the experimentally achieved results, the proposed method outperformed the mainstream livestock product price forecasting method in forecasting accuracy, trend forecasting and method convergence.