Year: 2025
Type: article
Abstract: 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.
DOI: 10.54097/2r2r9j24
Authors: Yuanzhe Li, Jin Li
Institution: Hebei University of Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4415140922