Forecasting Automobile Consumer Behavior: A Long Short-Term Memory Network Model

Abstract

Long Short-Term Memory (LSTM) networks have demonstrated strong predictive capabilities across a wide range of domains. In the automotive sector, accurately forecasting user consumption trends can significantly enhance enterprise decision-making. By applying LSTM networks to conduct in-depth mining and analysis of automobile user consumption data, it becomes possible to capture complex temporal dependencies inherent in consumption patterns. Through systematic model training and optimization, effective prediction of automobile user consumption trends can be achieved. This capability provides a robust scientific foundation for the automotive industry to formulate targeted marketing strategies and plan product development, thereby strengthening market competitiveness.