基于SSA-ELM的大宗商品价格预测研究
发布时间：2018-08-25 15:42
【摘要】：随着经济全球化的发展,国际期货市场中各大类大宗商品价格波动剧烈,而全球经济形势不明朗以及货币政策不确定使得大宗商品期货价格难以被准确预测.本文选取玉米,原油,黄金分别作为大宗商品农产品类、能源类、金属类的代表对象,基于奇异谱分析方法(singular spectrum analysis,SSA),对商品期货价格进行分解,结合Kmeans动态聚类技术将分解量聚合成不同特征的价格序列,再采用具有优良特性的极限学习机算法(extreme learning machine,ELM)对模型进行训练,得到大宗商品期货价格预测模型.实证结果表明,采用序列分解聚类策略能够显著提高模型预测精度,在价格未来的整体水平和变动方向上都能达到较好的预测效果.
[Abstract]:With the development of economic globalization, commodity prices in international futures markets fluctuate sharply, while global economic uncertainty and monetary policy uncertainty make it difficult to accurately predict commodity futures prices. In this paper, corn, crude oil and gold are selected as the representative objects of commodity agricultural products, energy and metals, and the commodity futures prices are decomposed based on the singular spectrum analysis method (singular spectrum analysis,SSA). Combined with Kmeans dynamic clustering technology, the decomposed quantities are aggregated into price sequences with different characteristics, and then the model is trained by the extreme learning machine (extreme learning machine,ELM) algorithm with excellent characteristics, and the commodity futures price prediction model is obtained. The empirical results show that the prediction accuracy of the model can be improved significantly by using the sequence decomposition and clustering strategy, and the prediction results can be achieved in the overall level and the direction of change of the price in the future.
【作者单位】： 中国科学院数学与系统科学研究院;中国科学院国家数学与交叉科学中心;中国科学院大学;北京科技大学数理学院;
【基金】：国家自然科学基金(71271202) 中国科学院青年创新促进会项目~~
【分类号】：F713.35;TP18
，
本文编号：2203369
[Abstract]:With the development of economic globalization, commodity prices in international futures markets fluctuate sharply, while global economic uncertainty and monetary policy uncertainty make it difficult to accurately predict commodity futures prices. In this paper, corn, crude oil and gold are selected as the representative objects of commodity agricultural products, energy and metals, and the commodity futures prices are decomposed based on the singular spectrum analysis method (singular spectrum analysis,SSA). Combined with Kmeans dynamic clustering technology, the decomposed quantities are aggregated into price sequences with different characteristics, and then the model is trained by the extreme learning machine (extreme learning machine,ELM) algorithm with excellent characteristics, and the commodity futures price prediction model is obtained. The empirical results show that the prediction accuracy of the model can be improved significantly by using the sequence decomposition and clustering strategy, and the prediction results can be achieved in the overall level and the direction of change of the price in the future.
【作者单位】： 中国科学院数学与系统科学研究院;中国科学院国家数学与交叉科学中心;中国科学院大学;北京科技大学数理学院;
【基金】：国家自然科学基金(71271202) 中国科学院青年创新促进会项目~~
【分类号】：F713.35;TP18
，
本文编号：2203369
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