Research on Material Demand Forecasting Method for Discrete Manufacturing Industry Based on LSTM-MHKAN
WU Xiaofang
CHENG Yiying
YANG Meiyi
YANG Lei
Abstract：In industrial production,material demand is influenced by a myriad of factors,exhibiting highly complex and dynamic characteristics.These dynamic features include nonlinear relationships,short-term fluctuations,and potential long-term trends,which pose significant challenges to traditional forecasting methods.To address this issue,this study proposes an innovative material demand forecasting method,LSTM-MHKAN,which integrates Long Short-Term Memory networks(LSTM),Kolmogorov-Arnold Networks(KAN),and Multi-Head Attention(MHA)mechanisms.The method optimizes the forecasting process in three key steps.First,LSTM is utilized to capture temporal dependencies within the material demand data,identifying short-term variations and adjusting model parameters to accommodate dynamic fluctuations.Second,MHA is introduced to weight the LSTM outputs,enhancing the model's sensitivity to critical demand fluctuations.Finally,the KAN algorithm is applied to model the weighted attention outputs,capturing nonlinear relationships and adaptively forecasting future demand.Experimental results show that,compared to traditional forecasting algorithms,LSTM-MHKAN effectively reduces mean absolute error and mean absolute error,while improving R-Square.These results validate the effectiveness of LSTM-MHKAN in discrete manufacturing material demand prediction,providing strong decision support for cost reduction in the manufacturing industry.
Keywords：Kolmogorov-Arnold networklong short-term memorymaterial demand forecastingmulti-head attentiontime series forecasting
Publication Date：2025-04-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 48-58 )