Research on AGM Decision Fusion Based on LSTM Model and RF Model for Industrial Machine Demand Forecasting

The research on industrial machine demand forecasting aims to utilize data analysis and machine learning techniques to predict changes in market demand for industrial equipment. By analyzing historical sales data, market trends, and production capacity, a predictive model is established to assist enterprises in optimizing production plans and inventory management. This study not only enhances supply chain efficiency but also mitigates the risks of excess inventory and stockouts, thereby providing enterprises [1] with more precise demand forecasting. This, in turn, aids in enhancing market competitiveness and resource allocation efficiency. This study integrates two distinct prediction models: Long Short-Term Memory (LSTM) and Random Forest. Initially, the LSTM model and Random Forest are employed to forecast demand. Subsequently, the AGM decision attention mechanism is utilized to weight the combined predictions, ultimately yielding a refined demand forecast.