Developing grey prediction with Fourier series using genetic algorithms for tourism demand forecasting

Predicting the number of foreign tourists is significant for governments in devising development policies for tourism demand. Time series related to tourism often feature significant temporal fluctuation. Therefore, grey prediction in conjunction with the Fourier series for oscillating sequences is appropriate to foreign tourists forecasting. Grey prediction traditionally uses the ordinary least squares (OLS) to derive relevant parameters. However, as the conformance to statistical assumption is not guaranteed, estimators derived by using OLS may not be reliable. This study proposes an OLS-free grey model with the Fourier series by using soft computing techniques to determine the optimal parameters to maximize prediction accuracy. The experimental results demonstrate that the proposed grey prediction model performs well compared with other prediction models considered.