支持向量机(SVM)及其在场预测中的应用
APPLICATIONS OF SUPPORT VECTOR MACHINES IN THE FIELDS FORECASTING
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摘要: 介绍一种新的非线性回归分析方法——SVM回归。利用EOF能分解数据场和SVM回归分析可建立因子与预报量非线性关系的优势,设计预报方案:(1)将因子场和预报场分别用方差标准化、EOF场展开,提取两场时间系数;(2)用SVM回归分析实现因子场时间系数对预报场时间系数非线性预测;(3)由预测的预报场时间系数与对应空间函数反演原场。用交叉检验的方法,对1960～2003年1月热带海表温度场预报汛期(6～8月)华中区域降水场进行试验。SVM回归44年独立预报平均技巧评分10.4%,较随机预报具有明显的技巧水平,优于经典回归。Abstract: A new nonlinear regression,SVM regression,was introduced.With the superiority of both of EOF(Empirical orthogonal functions) separating fields and nonlinear SVM regression forecasting a program is projected:(1) factor fields and predicted fields are standardized,then EOF,and the time coefficients of two fields are extracted respectively;(2) with SVM regression the time coefficients of predicted fields are estimated by those of factor fields;(3) the original predicted fields are recovered by linear combination of the time coefficients and the eigenvectors.Summer rainfall over central China was predicted with January tropical sea surface temperature,and the cross-validations over 44 years were tested.The score is 10.4%,and this program is obviously superior in forecasting skills to both of random and classical regression.
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Key words:
- support vector machines /
- regression /
- estimate of fields
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