牡丹江师范学院学报（自然科学版）Issue(2)：11-16,26,7.
基于LSTM-SVM组合模型的氧化镥价格预测研究
Research on the Price Forecasting of Lanthanum Oxide Based on the LSTM-SVM Combined Model
摘要
Abstract
Constructing a combined LSTM-SVM model to predict the price of rare earth lutetium oxide.Monthly data from June 2013 to March 2023 is selected to build a nonlinear time series combined forecasting model integra-ting LSTM networks and SVM.The model combines LSTM's ability to capture long-term dependencies with SVM's robust generalization characteristics,and uses Grey Wolf Optimizer(GWO)for parameter optimization,demonstra-ting outstanding advantages in lutetium oxide price prediction.The model overcomes the shortcomings of single mod-els in processing nonlinear data and improves prediction accuracy.Experimental results show that the LSTM-SVM combined model achieves high precision,small errors,and excellent performance in lutetium oxide price prediction.关键词
稀土氧化镥/价格预测/LSTMKey words
rare earth lutetium oxide/price forecasting/LSTM分类
矿山工程引用本文复制引用
易金梅,赵旭,魏旌帆..基于LSTM-SVM组合模型的氧化镥价格预测研究[J].牡丹江师范学院学报（自然科学版）,2025,(2):11-16,26,7.基金项目
国家自然科学基金项目(61501197) （61501197）
