Forecasting Tourism Demand by a Novel Multi-Factors Fusion Approach

Abstract

The volatility of tourism demand is often caused by some irregular events in recent years. Typically, inbound tourists are quite sensitive to various factors, including the exchange rate fluctuation, consumer price index, personal or household income or consumption expenditure. We combine these multivariate time series data onto an ingenious multi-factor fusion strategy to contribute to precise tourism demand forecasting. A novel hybrid deep learning forecasting approach is developed by integrating several modules such as improved complete ensemble empirical mode decomposition with adaptive noise, intrinsic mode functions classification, multi-factors fusion and predictors matching. The monthly tourist flow data of Shanghai inbounding from USA, Korea and Japan are conducted to verify the performance of the proposed approach, which outperforms all benchmark models for different prediction horizons. The experimental results show that introducing external influencing factors can improve the prediction accuracy significantly, and therefore confirm the rationality and validity of the proposed approach.