基于EEMD组合模型的钢铁价格预测方法
Steel Price Forecasting Method Based on EEMD Combined Model
陆晓骏 1樊重俊 2梅亚光3
作者信息
- 1. 上海理工大学管理学院,上海 200093;欧冶云商数智供应链研发中心,上海 201999
- 2. 上海理工大学管理学院,上海 200093
- 3. 欧冶云商数智供应链研发中心,上海 201999
- 折叠
摘要
钢铁作为工业大宗商品的代表性商品,其价格研究可以帮助钢铁行业稳定发展.为了探索钢铁价格的变化规律,提出了一种基于集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)方法和Transformer注意力机制模型及自回归移动平均(Autoregressive Integrated Moving Average,ARIMA)模型的组合模型 EEMD-TRANSFORMER-ARIMA.将钢铁价格时序数据通过EEMD分解,对分解后的分量数据进行平稳性检测,并使用Transformer模型和ARIMA模型进行预测.实验选取6组典型的钢铁价格数据进行预测,结果表明该组合模型可以精确预测非平稳和非线性的时序数据,为钢铁价格分析提供了一种有效的预测方法,有助于辅助政府和企业进行市场决策.
Abstract
As a representative commodity of industrial commodities,steel price research can help the stable development of the steel industry.To explore the dynamics of steel prices,a new combined model named EEMD-TRANSFORMER-ARIMA is proposed,which is based on the Ensemble Empirical Mode Decomposition(EEMD)method and Transformer attention mechanism and Autoregressive Integrated Moving Average(ARIMA)model.EEMD decomposes the steel price time series data,the stability of the decomposed component data is tested,and the Transformer model and ARIMA model are used for prediction.The experiment indicates that the combined models take full advantage of the strong points of several single models and deal with non-stationary and non-linear time series.The result shows that EEMD-TRANSFORMER-ARIMA has excellent predicting performance for all six steel price data.It will be an effective method of prediction that will assist the government and enterprises in making market decisions.
关键词
钢铁价格预测/钢铁期货/集合经验模态分解/Transformer模型Key words
steel price forecasts/steel futures/ensemble empirical mode decomposition/Transformer model引用本文复制引用
基金项目
教育部哲学社会科学重大课题攻关项目(20JZD010)
出版年
2024
NETL
NSTL
万方数据