Predicting Production Capacity in Multiplex Networked Industrial Chains Based on Multi-Scale Dynamic Aggregation Network

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 resilience 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%.