GBDT based hierarchical model for commodity distribution prediction
ZHU Zhenfeng
TANG Jingyuan
CHANG Dongxia
ZHAO Yao
Abstract：Commodity prediction uses the previous commodity information to estimate and infer the future trends of the commodity,and it can be used for carrying out reasonable planning and distribution of commodity.To achieve accurate forecast of merchandise sales,a commodity distribution prediction model (HGBDT) based on Gradient Boosting Decision Tree (GBDT)is proposed.To alleviate the problem of dimensionality curse,we construct a Bagging based hierarchical ensemble learning model.The temporal-spatial property of commodity is exploited for characterizing commodity effectively,which is beneficial to boost the generalization of the learned prediction model.Experimental results on open dataset demonstrate the effectiveness of the proposed method.
Keywords：decision treeregression modelgradient boosting decision treeensemble learning
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 9-13,45 )