A novel two-step procedure for tourism demand forecasting

Abstract

ABSTRACT Tourism demand forecasting is a critical process in the planning of tourism utilities. In recent years, Internet search indices have been popularly used as tourism demand indicators. However, due to the complex relationship between tourism demand and the search index and the vast amounts of search engine data, the traditional econometric and artificial intelligence models could not be enough to complete the prediction task. Under this scenario, this paper proposes a novel two-step method to improve tourism demand prediction accuracy. Firstly, a double-boosting algorithm is proposed to select the keywords and their lags from the potential relevant high-dimensional search queries. Second, the ensemble Support Vector Regression (SVR) based Deep belief Network (DBN) approach is adopted to capture the possible non-linear relationship and to improve the forecasting performance through deep learning combination. The empirical results demonstrate that this procedure significantly outperforms other benchmark models when forecasting monthly Hong Kong tourist arrivals.