AI-based methods have been widely adopted in tourism demand forecasting. However, current AI-based methods are weak in capturing long-term dependency, and most of them lack interpretability. This study proposes a time series Transformer (Tsformer) with Encoder-Decoder architecture for tourism demand forecasting. The Tsformer encodes long-term dependencies with the encoder, merges the calendar of data points in the forecast horizon, and captures short-term dependencies with the decoder. Experiments on two datasets demonstrate that Tsformer outperforms nine baseline methods in short-term and long-term forecasting before and after the COVID-19 outbreak. Further ablation studies confirm that the adoption of the calendar of data points in the forecast horizon benefits the forecasting performance. Our study provides an alternative method for more accurate and interpretable tourism demand forecasting.
Yi et al. (Tue,) studied this question.