统计学与应用  >> Vol. 5 No. 3 (September 2016)

基于数据挖掘模型的锌期货价格预测模型
Zinc Futures Price Forecasting Model Based on Data Mining Model

DOI: 10.12677/SA.2016.53027, PDF, HTML, XML, 下载: 1,262  浏览: 4,792 

作者: 田永忠:云南铜业股份有限公司,云南 昆明

关键词: 锌期货数据挖掘模型随机森林Zinc Futures Data Mining Model Random Forest

摘要: 对期货市场的价格进行合理地预测,可以规避风险,获得收益。本文利用支持向量机(SVM)回归、决策树(RPART)回归、Bagging回归、Boosting回归、随机森林(Random forest)回归五种数据挖掘模型对锌期货的价格进行预测,预测结果良好,对一个月后锌期货价格变动方向的准确率在60%以上。
Abstract: Predicting futures market price reasonably can avoid risks and get benefit. In this paper, we used five kinds of data mining models—support vector machine (SVM) regression, decision trees re-gression, bagging regression, boosting regression, random forests regression—to predict zinc fu-tures price. It has got good results, whose accuracy rate can reach above 60% for predicting zinc futures price change direction within one month. 

文章引用: 田永忠. 基于数据挖掘模型的锌期货价格预测模型[J]. 统计学与应用, 2016, 5(3): 276-280. http://dx.doi.org/10.12677/SA.2016.53027