Demand Forecasting and Cross Elasticity Analysis Based on Stochastic Processes

This study investigates a dynamic demand forecasting model based on stochastic processes, employing adaptive filtering and multilevel correlation analysis for regression forecasting of time-series data. The model utilizes cross-elasticity principles to construct a demand response structure and systematically analyzes elasticity fluctuations across multiple periods and states. By incorporating risk control and sensitivity analysis, the model evaluates decision robustness under market uncertainty. Results demonstrate that the model has strong adaptability in handling high-dimensional data, providing a theoretical foundation for complex time-series demand forecasting.