A Study on Agricultural Commodity Price Prediction Model Based on Secondary Decomposition and Long Short-Term Memory Network

Changxia Sun, Menghao Pei, Bo Cao, Saihan Chang and Haiping Si ()
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Changxia Sun: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Menghao Pei: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Bo Cao: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Saihan Chang: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Haiping Si: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China

Agriculture, 2023, vol. 14, issue 1, 1-22

Abstract: In order to address the significant prediction errors resulting from the substantial fluctuations in agricultural product prices and the non-linear features, this paper proposes a hybrid forecasting model based on variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD), and long short-term memory networks (LSTM). This combined model is referred to as the VMD–EEMD–LSTM model. Initially, the original time series of agricultural product prices undergoes decomposition using VMD to obtain a series of variational mode functions (VMFs) and a residual component with higher complexity. Subsequently, the residual component undergoes a secondary decomposition using EEMD. All components are then fed into an LSTM model for training to obtain predictions for each component. Finally, the predictions for each component are linearly combined to generate the ultimate price forecast. To validate the effectiveness of the VMD–EEMD–LSTM model, empirical analyses were conducted for one-step and multi-step forecasts using weekly price data for pork, Chinese chives, shiitake mushrooms, and cauliflower from China’s wholesale agricultural markets. The results indicate that the composite model developed in this study provides enhanced forecasting accuracy.

Keywords: price forecasting; dual decomposition; variational mode decomposition; ensemble empirical mode decomposition; long short-term memory network (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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