1.Introduction China has announced that it will adopt forceful policies and measures and strive to achieve peak carbon dioxide(CO_(2))emissions before 2030 and carbon neutrality before 2060—aims that are largely cons...1.Introduction China has announced that it will adopt forceful policies and measures and strive to achieve peak carbon dioxide(CO_(2))emissions before 2030 and carbon neutrality before 2060—aims that are largely consistent with the goal to limit warming to 1.5C[1].Achieving this target requires the deep decarbonization of China’s entire economy,with a particular focus on coal-fired power plants(CFPPs).展开更多
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, go...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 pre- diction 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.展开更多
基金This work was supported by the major consulting research projects of the Chinese Academy of Engineering“Research on the Strategy of Carbon Sequestration and Resource Utilization,”the Ministry of Education of Humanities and Social Science project(21YJC630009)the National Natural Science Foundation of China(72104116,72025401,71974108,and 71690244)the Tsinghua University-INDITEX Sustainable Development Fund.
文摘1.Introduction China has announced that it will adopt forceful policies and measures and strive to achieve peak carbon dioxide(CO_(2))emissions before 2030 and carbon neutrality before 2060—aims that are largely consistent with the goal to limit warming to 1.5C[1].Achieving this target requires the deep decarbonization of China’s entire economy,with a particular focus on coal-fired power plants(CFPPs).
基金supported by grants from the National Natural Science Foundation of China(Grant No.71173200)the Strategic Research Center for Oil and Gas Resources,Ministry of Land and Resources of the People's Republic of China(Grant No.2014BJYQ03)
文摘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 pre- diction 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.
