作者:Yu, LA (Yu, Lean)[ 1,2 ] ; Zhao, YQ (Zhao, Yaqing)[ 1 ] ; Tang, L (Tang, Ling)[ 3 ] ; Yang, ZB (Yang, Zebin)[ 4 ]
INTERNATIONAL JOURNAL OF FORECASTING
卷: 35期: 1页: 213-223
DOI: 10.1016/j.ijforecast.2017.11.005
出版年: JAN-MAR 2019
文献类型:Article
摘要
The rapid development of big data technologies and the Internet provides a rich mine of online big data (e.g., trend spotting) that can be helpful in predicting oil consumption - an essential but uncertain factor in the oil supply chain. An online big data-driven oil consumption forecasting model is proposed that uses Google trends, which finely reflect various related factors based on a myriad of search results. This model involves two main steps, relationship investigation and prediction improvement. First, cointegration tests and a Granger causality analysis are conducted in order to statistically test the predictive power of Google trends, in terms of having a significant relationship with oil consumption. Second, the effective Google trends are introduced into popular forecasting methods for predicting both oil consumption trends and values. The experimental study of global oil consumption prediction confirms that the proposed online big-data-driven forecasting work with Google trends improves on the traditional techniques without Google trends significantly, for both directional and level predictions. (C) 2017 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
关键词
作者关键词:Google trends; Oil consumption forecasting; Online big data; Supply chain; Artificial intelligence
KeyWords Plus:ARTIFICIAL NEURAL-NETWORK; EXTREME LEARNING-MACHINE; MULTIOBJECTIVE OPTIMIZATION; ENERGY-CONSUMPTION; SHORT-RUN; DEMAND; PRICE; CLASSIFICATION; IMPROVEMENT; PREDICTION
通讯作者地址:
Beihang University Beihang Univ, Sch Econ & Management, 37 Xueyuan Rd, Beijing 100191, Peoples R China.
通讯作者地址: Tang, L (通讯作者)
