饲料研究2025，Vol.48Issue(2)：227-231,5.DOI:10.13557/j.cnki.issn1002-2813.2025.02.041
基于集成学习的ARIMA-LSTM模型在棉粕价格预测中的应用
Application of ARIMA-LSTM model based on ensemble learning in cottonseed meal price prediction
摘要
Abstract
Accurate prediction of cottonseed meal prices is crucial for stabilizing the supply of livestock products,promoting the sustainable development of the feed processing industry,and ensuring national food security.This study aims to construct a cottonseed meal price prediction model based on the deep learning mechanism of the Long Short-Term Memory(LSTM)neural network.Initially,the study employs the Autoregressive Integrated Moving Average(ARIMA)model to forecast the linear changes in time series data,and then applies the LSTM algorithm to estimate the nonlinear effects in the cottonseed meal price series.The Extreme Gradient Boosting(XGBoost)algorithm,an ensemble learning method,is used to determine the lag length of the residual sequence as the input node in the LSTM model.Finally,the sum of the fitted linear and nonlinear changes is taken as the final prediction value of the ARIMA-LSTM hybrid model.The study indicates that the ARIMA-LSTM hybrid model based on the XGBoost algorithm outperforms the single ARIMA time series prediction model and demonstrates good forecasting performance.关键词
深度学习/棉粕价格预测/集成学习/ARIMA模型/XGBoost算法Key words
deep learning/cottonseed meal price prediction/ensemble learning/ARIMA model/XGBoost algorithm分类
农业科技引用本文复制引用
吴展,王春晓..基于集成学习的ARIMA-LSTM模型在棉粕价格预测中的应用[J].饲料研究,2025,48(2):227-231,5.基金项目
国家现代农业产业技术体系(项目编号:CARS-47) （项目编号:CARS-47）
