Predicting Production Capacity in Multiplex Networked Industrial Chains Based on Multi-Scale Dynamic Aggregation NetworkOA
With the high degree of integration of production capacity in the industrial field,the original form of single,linear,and vertical cooperation between different industrial chains has been broken,and a multiplex networked industrial chain has been formed.Traditional time series forecasting methods are often prone to fall into the trap of computational volume caused by long historical information and the problem of dimensional explosion caused by the mixing of redundant information in the face of multiple networked industrial chain capacity forecasting with large data volume and high information dimensionality.In this paper,we first propose an information decoupling technique based on the principle of time series decomposition to provide more accurate cyclical forecasting results for capacity forecasting.Secondly,this paper introduces a multi-scale dynamic aggregation network technique.This technique dynamically aggregates and predicts variables at different time scales.The combination of these two approaches is adept at capturing a wider range of local and global trends,thereby greatly improving the accuracy and robustness of forecasting models.In this paper,experiments are conducted to compare with the current mainstream time series prediction algorithms.The results show that in multivariate long time series,the error of our algorithm is reduced by 27.8%.
Pan Li;Kai Di;Fulin Chen;Yuanshuang Jiang;Yuangan Wang;Yichuan Jiang;Dan Chen
School of Cyber Science and Engineering,Southeast University,Nanjing 211189,ChinaSchool of Computer Science and Engineering,Southeast University,Nanjing 211189,ChinaSchool of Cyber Science and Engineering,Southeast University,Nanjing 211189,ChinaSchool of Cyber Science and Engineering,Southeast University,Nanjing 211189,ChinaCollege of Electronics and Information Engineering,Beibu Gulf University,Qinzhou 535000,ChinaSchool of Cyber Science and Engineering,Southeast University,Nanjing 211189,ChinaCollege of Electronics and Information Engineering,Beibu Gulf University,Qinzhou 535000,China
管理科学
time series forecastingmulti-scale dynamic aggregationmultiplex networked industrial chains
《Tsinghua Science and Technology》 2026 (3)
P.1881-1893,13
supported by the National Key Research and Development Programn of China(No.2022YFB3304400)the National Natural Science Foundation of China(Nos.62476121,62303111,62076060,and 61932007)Guangxi Science and Technology Major Program(No.AA24206003)the Key Research and Development Program of Guangxi(No.AB2410317)the Key Research and Development Program of Jiangsu Province of China(No.BE2022157)the Defense Industrial Technology Development Program(No.JCKY2021214B002)the Fellowship of China Postdoctoral Science Foundation(No.2022M720715)the Open Funds of KuiYuan Laboratory(No.KY202432).
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