ArticleScientific reports2025
Time series transformer for tourism demand forecasting.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
3 authors.
Funding
Abstract
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.
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