A multi-scale method for forecasting oil price with multi-factor search engine data. (1st January 2020)

• Record Type: Journal Article
• Authors: Tang, Ling; Zhang, Chengyuan; Li, Ling; Wang, Shouyang
• Is Part Of: Applied energy. Volume 257(2020)
• Publication Date: 2020-01-01
• Subjects: Big data -- Search engine data -- Google trends -- Multivariate empirical mode decomposition -- Oil price forecasting; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.114033
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: General framework of the proposed multi-scale methodology with multi-factor SED. Highlights: A multi-scale method with search engine data is built to forecast oil price. 3 steps are taken: multi-factor data process, multi-scale analysis and prediction. Search engine data for diverse oil-related factors is used as powerful predictors. Multi-scale relationship between oil price and search engine data is investigated. Empirical study verifies its superiority over both popular and similar benchmarks. Abstract: 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.