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

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 (2016) 基于数据挖掘模型的锌期货价格预测模型。 统计学与应用, 5, 276-280. doi: 10.12677/SA.2016.53027

参考文献

[1] Mcclean, S., Scotney, B. and Shapcott, M. (2000) Incorporating Domain Knowledge into Attribute-Oriented Data Mining. International Journal of Intelligent Systems, 15, 535-547.
http://dx.doi.org/10.1002/(SICI)1098-111X(200006)15:6<535::AID-INT4>3.0.CO;2-9

[2] Hassan, M.R., Nath, B. and Kirley, M. (2007) A Fusion Model of HMM, ANN and GA for Stock Market Forecasting. Expert Systems with Applications, 33, 171-180.
http://dx.doi.org/10.1016/j.eswa.2006.04.007

[3] 杨国梁, 赵社涛, 徐成贤. 基于支持向量机的金融市场指数追踪技术研究[J]. 国际金融研究, 2009(10): 68-72.

[4] 冯建, 邱菀华. 一种基于信息熵的金融数据神经网络分类方法[J]. 控制与决策, 2012, 27(2): 211-215.

分享
Top