Research on Ice and Snow Tourism Economic Forecasting Method Based on Improved Gated Recurrent Unit Model

Ice and snow tourism economics exhibit significant seasonality and complex nonlinear characteristics, challenging traditional forecasting methods. The computer-based technologies show a great significant potential in this field due to its powerful computation ability. This paper investigates an advanced economic forecasting approach using the Gated Recurrent Unit (GRU) model, renowned for managing both long-term and short-term dependencies in sequential data. By analyzing factors influencing ice and snow tourism economics and collecting comprehensive economic data, the study aims to enhance prediction accuracy. The GRU model is optimized through a series of processes, including fine-tuning the model architecture, hyperparameter tuning via grid and random search, and integrating exogenous variables such as meteorological data and holiday information. Feature engineering techniques transform and scale the data, while advanced training techniques like early stopping and cross-validation improve generalization. The model is rigorously validated using Harbin, China's 2015-2021 ice and snow tourism data, with experimental results indicating that the GRU-based method substantially improves accuracy in capturing economic fluctuations compared to traditional time series models. This study highlights the GRU model's robustness and potential to significantly support scientific management and decision-making in the ice and snow tourism sector, ensuring more reliable and precise economic forecasts.