The Hotel Occupancy Rate Forecasting Based on GRU-transformer Considering Multi-time Scale Features
FU Li1, WANG Xiqiong2
1. School of Economics and Management, Taiyuan Normal University, Jinzhong 030619, China; 2. College of Culture and Tourism, Luzhou Vocational and Technical College, Luzhou 646000, China
Abstract：In view of the current situation of difficult data acquisition, lagging forecasting and insufficient periodicity and continuity of data mining in hotel occupancy prediction, a hybrid deep learning method of GRU-Transformer considering multi-time scale features is proposed. First, model the occupancy rate data feature matrix of year, month and day cycle time segments, and construct the input data of the model. Then, a parallel computing structure combining Attention-GRU and Conv-Transformer modules is constructed to excavate the periodic and continuity characteristics of the data and output the predicted value after integrating the characteristics. Finally, the historical occupancy data of two hotels are optimized and the ablation and comparison experiments are carried out. Experiments show that the algorithm proposed in this paper can greatly improve the prediction accuracy, and can fully meet the time requirement of timeliness. It can be used for real-time prediction of hotel occupancy data, help hotel managers timely adjust business strategy and improve hotel competitiveness.
付丽, 王西琼. 考虑多时间尺度特征的GRU-Transformer酒店入住率预测[J]. 复杂系统与复杂性科学, 2026, 23(3): 152-160.
FU Li, WANG Xiqiong. The Hotel Occupancy Rate Forecasting Based on GRU-transformer Considering Multi-time Scale Features[J]. Complex Systems and Complexity Science, 2026, 23(3): 152-160.
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