【摘要】：隨著經(jīng)濟(jì)全球化的發(fā)展,國際期貨市場中各大類大宗商品價格波動劇烈,而全球經(jīng)濟(jì)形勢不明朗以及貨幣政策不確定使得大宗商品期貨價格難以被準(zhǔn)確預(yù)測.本文選取玉米,原油,黃金分別作為大宗商品農(nóng)產(chǎn)品類、能源類、金屬類的代表對象,基于奇異譜分析方法(singular spectrum analysis,SSA),對商品期貨價格進(jìn)行分解,結(jié)合Kmeans動態(tài)聚類技術(shù)將分解量聚合成不同特征的價格序列,再采用具有優(yōu)良特性的極限學(xué)習(xí)機(jī)算法(extreme learning machine,ELM)對模型進(jìn)行訓(xùn)練,得到大宗商品期貨價格預(yù)測模型.實證結(jié)果表明,采用序列分解聚類策略能夠顯著提高模型預(yù)測精度,在價格未來的整體水平和變動方向上都能達(dá)到較好的預(yù)測效果.
[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.
【作者單位】： 中國科學(xué)院數(shù)學(xué)與系統(tǒng)科學(xué)研究院;中國科學(xué)院國家數(shù)學(xué)與交叉科學(xué)中心;中國科學(xué)院大學(xué);北京科技大學(xué)數(shù)理學(xué)院;
【基金】：國家自然科學(xué)基金(71271202) 中國科學(xué)院青年創(chuàng)新促進(jìn)會項目~~
【分類號】：F713.35;TP18
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本文編號：2203369