Study on the Logistical Forecasting Method based on Rough Set Theory and Support Vector Machine( SVM)
Zhong Ying-hong
Huang Xin
Abstract：Proper forecasting models are of strategic significance for upgrading and optimizing logistical in-dustry.Common forecasting methods include increasing rate method, moving average method, time series method, etc.In real applications, many forecasting methods are not so accurate as to ensure validity be-cause logistical data have such features as multi attributes ( including redundant attributes) , non-linear, small sample.A method is proposed that eliminates redundant attributes for reduction based on discern-ibility matrix algorithm with rough set theory improving the SVM model and optimizing the input parameters by genetic algorithm.Detailed steps of the forecasting method are provided and validity examined via cargo data of Guangdong province.
Keywords：logistical demand forecastingattribute reduction by rough set theorysupport vector machine ( SVM)
Publication Date：2015-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 28-33 )