Tensor Migration Guided Attention BI-LSTM Prediction Model
ZHU Wenya
CHEN Yiming
XU Yinan
Abstract：Spare parts demand forecast is one of the important means of lean management and economic benefit promotion.Aiming at the problems of insufficient data utilization and low accuracy of traditional prediction model,a new method is proposed to forecast the demand of spare parts.Firstly,the maximum mean difference(MMD)method is used to filter the data of migrating source domains to reduce the possibility of information redundancy caused by the migration of multiple similar source domains.Then,the Attention mechanism and bidirectional ordered short and long-term memory network are integrated to construct the Atten-tion BI ON-LSTM prediction model.The past and future information can be fully applied through the Attention BI ON-LSTM model.And the weights of different feature vectors are calculated to prevent the feature containing important information from disappearing.Finally,by tensor migration,the tensor of source domain training is extracted to initialize the target domain model,and the target domain prediction results are obtained after fine tuning.Experimental results show that the proposed method is superior to the com-mon time series prediction model.
Keywords：maximum mean differenceAttention mechanismbidirectional ordered short and long time memory networktensor migrationtime series prediction
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：6( 3372-3377 )