摘要
Abstract
Natural uranium is the indispensable mineral material foundation for the development of nuclear power,which significantly impacts the development growing speed and operating costs of nuclear power.Hence,the international natural uranium price(abbreviated as natural uranium price)forecast work has important application value for developing nuclear power.The data-driven model is one of the currently most effective price prediction methods.It can forecast the trend and specific value of future natural uranium prices by analyzing the characteristics of past natural uranium prices and covariate data.Generally,the improvement should commence from two aspects of data content and model structure to promote the data-driven model's forecast performance.Firstly,an in-depth analysis was conducted on the covariate factors that affect natural uranium prices.The covariate factors of natural uranium prices were divided into three categories:basic supply-demand relationship,market transaction situation,and economic and financial environment.A natural uranium database was constructed by selecting relatively comprehensive covariate data,and the validation was verified by correlation tests.Then,based on the covariate data·s autocorrelation on time dimension and cross-correlation between covariables,the convolutional neural network(CNN)was introduced,based on the model fusion method of machine learning,to perfect the forecast model·s ability to analyze data features.Finally,five different model fusion structures were formed on the basis of the long and short-term memory(LSTM)network,and the future prices were forecasted respectively.The comparison of sensitivity testing results revealed the CNN-inserted LSTM-CNN-LSTM model has better natural uranium price forecast performance and is less susceptible to the ahead forecast steps and time window.This research demonstrates the short-term natural uranium prices of the next six months can be stably and precisely forecasted through the data-driven model constructed by model fusion and sensitivity testing method,furthermore,provides data reference for nuclear power development and operation.关键词
国际天然铀/价格预测/模型融合/供需关系Key words
international natural uranium/price forecast/model fusion/supply-demand relationship分类
天文与地球科学
