This paper describes an equation discovery approach based on machine learning using LAGRAMGE as an equation discovery tool, with two sources of input, a dataset and model presented in context-free grammar. The approac...This paper describes an equation discovery approach based on machine learning using LAGRAMGE as an equation discovery tool, with two sources of input, a dataset and model presented in context-free grammar. The approach is searching a large range of po- tential equations by a specific inodel. The parameters of the equation are fitted to find the best equations. The experiments are illustratedwith commodity prices from the London Metal Exchange for the period of January-October 2009. The outputs of the experiments are a large mumber of equations; some of the equations display that the predicted prices are following the market trends in perfect patterns.展开更多
降雨数值预报在预防极端天气和其他气象事件方面具有重要作用。通过提供可靠的概率预报,可以更准确地描述预报结果的不确定性,为决策者提供科学依据,从而提高应用价值和经济价值。以寸滩-三峡区间为研究对象,根据TIGGE资料中的ECMWF和NC...降雨数值预报在预防极端天气和其他气象事件方面具有重要作用。通过提供可靠的概率预报,可以更准确地描述预报结果的不确定性,为决策者提供科学依据,从而提高应用价值和经济价值。以寸滩-三峡区间为研究对象,根据TIGGE资料中的ECMWF和NCEP 2种模式,对2020-2022年(5-10月)逐日降水集合预报信息的精度进行评估。首先,采用Talagrand分布和Brier评分来评估不同预见期的集合预报能力;然后,采用贝叶斯模型平均(Bayesian model averaging,BMA)来修正集合预报;最后,对不同预见期的BMA修正值和实际降雨值之间的误差进行分析。结果显示:ECMWF和NCEP 2种模式的预报能力随预见期的增加逐渐下降,在不同预见期下,通过BMA修正后的降雨预报值具有更高的精度。展开更多
文摘This paper describes an equation discovery approach based on machine learning using LAGRAMGE as an equation discovery tool, with two sources of input, a dataset and model presented in context-free grammar. The approach is searching a large range of po- tential equations by a specific inodel. The parameters of the equation are fitted to find the best equations. The experiments are illustratedwith commodity prices from the London Metal Exchange for the period of January-October 2009. The outputs of the experiments are a large mumber of equations; some of the equations display that the predicted prices are following the market trends in perfect patterns.
文摘降雨数值预报在预防极端天气和其他气象事件方面具有重要作用。通过提供可靠的概率预报,可以更准确地描述预报结果的不确定性,为决策者提供科学依据,从而提高应用价值和经济价值。以寸滩-三峡区间为研究对象,根据TIGGE资料中的ECMWF和NCEP 2种模式,对2020-2022年(5-10月)逐日降水集合预报信息的精度进行评估。首先,采用Talagrand分布和Brier评分来评估不同预见期的集合预报能力;然后,采用贝叶斯模型平均(Bayesian model averaging,BMA)来修正集合预报;最后,对不同预见期的BMA修正值和实际降雨值之间的误差进行分析。结果显示:ECMWF和NCEP 2种模式的预报能力随预见期的增加逐渐下降,在不同预见期下,通过BMA修正后的降雨预报值具有更高的精度。
