关键词: 低温, 低温

Abstract:

To construct a novel nonlinear multidimensional time series analysis approach named as GS-SVR based on geostatistics (GS) and support vector machine regression (SVR), which could provide a fast order determination and show the dynamic characteristics of dataset as well as the effect of environmental factors. Firstly, stabilizing the time series and estimating the order by geostatistics. Secondly, screening the variable by leave-one-out method based on SVR and eliminating redundant information of variables by principal component analysis (PCA). Lastly, the reliability of GS-SVR was tested by two agricultural datasets with one-step prediction. The prediction results showed that GS-SVR had the higher prediction precision and stability compared with all reference models. As a novel nonlinear multidimensional time series analysis approach integrating time series analysis with regression analysis, GS-SVR had order determination quickly and accurately and higher prediction precision. It can be widely used in the prediction area of agriculture, ecology and economics.