In this Deep Dive, we explore Soulformer, a new time-series transformer that targets both accuracy and interpretability in forecasting tourism demand. We unpack how its encoder–decoder structure, attention mechanisms, calendar features, and smart masking capture long-term patterns while keeping insights visible through attention visualizations. We’ll review real-world tests on Jiuzhaigou Valley and Siguniang Mountain in China—covering pre- and post-COVID periods—where Soulformer consistently outperformed ARIMA, LSTM, and other baselines, and discuss future directions like incorporating real-time events and social sentiment.

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