Tourism demand forecasting under conceptual drift during COVID-19: an ensemble deep learning model

To address the issue of tourism demand forecasting in the context of concept drift, a new ensemble deep learning model based on transformer is proposed, which includes three parts: data processing, base predictor pool construction, base predictor selection and combination. In the first part, the relevant data are collected and converted into the input form required by transformer. In the second part, a base predictor pool containing multiple predictors is constructed, where each predictor can capture a specific concept from historical data. In the final part, a predictor selection algorithm is proposed to select 'effective predictors' from the base predictor pool. These effective predictors are further integrated to generate the final forecasts. The proposed model is applied to the forecast of tourist volume of two attractions in China. The results show that the proposed model outperforms the benchmark models in the context of concept drift, benchmarked against eight models.