  AI-based methods have been widely applied to tourism demand forecasting.
However, current AI-based methods are short of the ability to process long-term
dependency, and most of them lack interpretability. The Transformer used
initially for machine translation shows an incredible ability to long-term
dependency processing. Based on the Transformer, we proposed a time series
Transformer (Tsformer) with Encoder-Decoder architecture for tourism demand
forecasting. The proposed Tsformer encodes long-term dependency with encoder,
captures short-term dependency with decoder, and simplifies the attention
interactions under the premise of highlighting dominant attention through a
series of attention masking mechanisms. These improvements make the multi-head
attention mechanism process the input sequence according to the time
relationship, contributing to better interpretability. What's more, the context
processing ability of the Encoder-Decoder architecture allows adopting the
calendar of days to be forecasted to enhance the forecasting performance.
Experiments conducted on the Jiuzhaigou valley and Siguniang mountain tourism
demand datasets with other nine baseline methods indicate that the proposed
Tsformer outperformed all baseline models in the short-term and long-term
tourism demand forecasting tasks. Moreover, ablation studies demonstrate that
the adoption of the calendar of days to be forecasted contributes to the
forecasting performance of the proposed Tsformer. For better interpretability,
the attention weight matrix visualization is performed. It indicates that the
Tsformer concentrates on seasonal features and days close to days to be
forecast in short-term forecasting.
