Accurately Forecasting Model for the Stochastic Volatility Data in Tourism Demand

This study attempts to enhance the effectiveness of stochastic volatility data. This work presents an empirical case involving the forecasting of tourism demand to demonstrate the efficacy of the accuracy forecasting model. Work combining the grey forecasting model (GM) and Fourier residual modification model to refine the forecasting effectiveness for the stochastic volatility data, which can estimate fluctuations in historical time series. This study makes the following contributions: 1) combining the grey forecasting and Fourier residual modification models to refine the forecasting effectiveness for the stochastic volatility data, 2) providing an effective method for forecasting the number of international visitors to Taiwan, 3) improving the accuracy of short-term forecasting in cases involving sample data with significant fluctuations.