文章基本信息

标题：A new approach for crude oil price prediction based on stream learning
本地全文：下载
作者：Shuang Gao ; Shuang Gao ; Yalin Lei 等
期刊名称：Geoscience Frontiers
印刷版ISSN：1674-9871
出版年度：2017
卷号：8
期号：1
页码：183-187
DOI：10.1016/j.gsf.2016.08.002
语种：English
出版社：Elsevier
摘要：Abstract Crude oil is the world's leading fuel, and its prices have a big impact on the global environment, economy as well as oil exploration and exploitation activities. Oil price forecasts are very useful to industries, governments and individuals. Although many methods have been developed for predicting oil prices, it remains one of the most challenging forecasting problems due to the high volatility of oil prices. In this paper, we propose a novel approach for crude oil price prediction based on a new machine learning paradigm called stream learning. The main advantage of our stream learning approach is that the prediction model can capture the changing pattern of oil prices since the model is continuously updated whenever new oil price data are available, with very small constant overhead. To evaluate the forecasting ability of our stream learning model, we compare it with three other popular oil price prediction models. The experiment results show that our stream learning model achieves the highest accuracy in terms of both mean squared prediction error and directional accuracy ratio over a variety of forecast time horizons. Graphical abstract Display Omitted Highlights • Proposing a new approach for oil price prediction based on stream learning. • Updating the model whenever new oil price data are available to capture the changing pattern of oil prices. • Achieving the highest accuracy compared with 3 popular oil price prediction models.
关键词：KeywordsenCrude oilEconomic geologyPrediction modelMachine learningStream learning