论文标题：A multi-scale method for forecasting oil price with multi-factor search engine data

　　发表时间：2020

　　论文所有作者：Tang Ling, Zhang Chengyuan, Li Ling, Wang Shouyang.

　　期刊名及所属分类：Applied Energy(国际SSCI or SCI)

　　英文摘要：With the boom in big data, a promising idea for using search engine data has emerged and improved international oil price prediction, a hot topic in the fields of energy system modelling and analysis. Since different search engine data drive the oil price in different ways at different timescales, a multi-scale forecasting methodology is proposed that carefully explores the multi-scale relationship between the oil price and multi-factor search engine data. In the proposed methodology, three major steps are involved: (1) a multi-factor data process, to collect informative search engine data, reduce dimensionality, and test the predictive power via statistical analyses; (2) multi-scale analysis, to extract matched common modes at similar timescales from the oil price and multi-factor search engine data via multivariate empirical mode decomposition; (3) oil price prediction, including individual prediction at each timescale and ensemble prediction across timescales via a typical forecasting technique. With the Brent oil price as a sample, the empirical results show that the novel methodology significantly outperforms its original form (without multi-factor search engine data and multi-scale analysis), semi-improved versions (with either multi-factor search engine data or multi-scale analysis), and similar counterparts (with other multi-scale analysis), in both the level and directional predictions.

　　中文摘要：随着大数据的蓬勃发展，一个利用搜索引擎数据的有前途的想法出现了，并改善了国际油价预测，这是能源系统建模和分析领域的热门话题。由于不同的搜索引擎数据在不同的时间尺度上对油价有不同的驱动作用，本文提出了一种多尺度预测方法，详细探讨了油价与多因素搜索引擎数据之间的多尺度关系。该方法主要包括三个步骤:(1)多因素数据处理，收集信息搜索引擎数据，降低维数，并通过统计分析测试预测能力;(2)多尺度分析，通过多因素经验模态分解，从油价和多因素搜索引擎数据中提取相似时间尺度下的匹配共模态;(3)石油价格预测，通过一种典型的预测技术，包括每个时间尺度的个体预测和跨时间尺度的集合预测。布伦特油价为例,实证结果表明,该新方法明显优于原来的形式(没有多因素和多尺度分析搜索引擎数据),半改良版本(与多因素的搜索引擎数据或多尺度分析),和类似的同行(与其他多尺度分析)、水平和方向的预测。