Comparative study on red jujube futures price forecasting methods
The trading of dried jujube futures has significantly contributed to stabilizing prices,particularly in Southern Xinjiang.Examining methods for predicting jujube futures prices can aid stakeholders in the industry to streamline production,processing,and investment strategies.This paper assesses three prediction models for jujube futures prices:long short-term memory(LSTM)neural network,support vector regression(SVR),and back propagation(BP)neural network.Results indicate that compared to SVR,the LSTM model reduces the root mean square error(RMSE)by 17.4%and the mean absolute percentage error(MAPE)by 25%.Compared to BP,LSTM reduces RMSE by 12.8%and MAPE by 33.3%.LSTM performs better for annual price prediction,especially in forecasting jujube futures trends five days ahead.The LSTM-based model can enhance decision-making in jujube futures price forecasting.
red jujubefuturesprice forecastLSTM neural networkSVR modelBP neural network
万方数据
维普
