Affiliations:
College of Information Engineering, Sichuan Agricultural University, Sichuan, China
Correspondence:
[*]
Corresponding author. Yan Guo, College of Information Engineering, Sichuan Agricultural University, Sichuan, China. E-mail: 14403@sicau.edu.cn.
Note: [1] These authors contributed equally to this work, and should be regarded as co-first authors.
Abstract: Under the influence of the coronavirus disease and other factors, agricultural product prices show non-stationary and non-linear characteristics, making it increasingly difficult to forecast accurately. This paper proposes an innovative combinatorial model for Chinese hog price forecasting. First, the price is decomposed using the Seasonal and Trend decomposition using the Loess (STL) model. Next, the decomposed data are trained with the Long Short-term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Finally, the prepared data and the multivariate influence factors after Factor analysis are predicted using the gated recurrent neural network and attention mechanisms (AttGRU) to obtain the final prediction values. Compared with other models, the STL-FA-AttGRU model produced the lowest errors and achieved more accurate forecasts of hog prices. Therefore, the model proposed in this paper has the potential for other price forecasting, contributing to the development of precision and sustainable agriculture.
Keywords: Machine learning, precision agriculture, digital agriculture, STL, attentional mechanisms
