Agricultural commodity futures prices prediction based on a new hybrid forecasting model combining quadratic decomposition technology and LSTM model

Abstract

The stability of agricultural futures market is of great significance to social economy and agri-cultural development. In view of the complexity of the fluctuation of agricultural futures prices, it is challenging to make up for the shortcomings of the existing data preprocessing technology so as to improve the prediction accuracy of the model. This paper puts forward a new VMD-SGMD-LSTM model based on improved quadratic decomposition technology and artificial intelligence model. First of all, in the data preprocessing part, VMD is used to decompose the original futures price data, and SGMD is used to further process the remaining components. Secondly, the LSTM model is used to predict a series of modal components, and the final result is obtained by synthesizing the predicted values of different components. Furthermore, based on the futures trading data of wheat, corn and sugar in China agricultural futures market, this paper makes an empirical study in the 1-step, 2-step and 4-step ahead forecasting scenarios, respectively. The results show that compared with other benchmark models, the VMD-SGMD-LSTM hybrid model proposed in this paper has better forecasting ability and robustness for different agricultural futures, which effectively makes up for the shortcomings of existing research.