Wheat futures price prediction based on GA-VMD-CS-LSTM model

Abstract

Wheat is an important agricultural product. Its price discovery is of great significance to the transaction efficiency of the futures market and even the price fluctuation of the supply and demand relationship in the spot market. Due to the futures price fluctuation itself being a nonlinear relationship, in front of massive data, to achieve a better prediction effect, this paper uses the GA-VMD method to clean the data, and the CS-LSTM model to predict the futures price of wheat in Zhengzhou Commodity Institute. Comparing with the prediction results of SVR, BP and LSTM, it is found that CS-LSTM has lower prediction error and better prediction effect. This paper studies the changes in wheat futures prices and provides theoretical support for government to timely specify appropriate policies to adjust wheat output and prices and improve farmers' lives.