针对当前酒店入住率预测问题中数据难获取,预测具有滞后性,挖掘数据周期性与连续性特征不充分的现状,提出一种考虑多时间尺度特征的GRU-Transformer混合深度学习方法。首先对年、月、日周期时间片段入住率数据特征矩阵进行建模,构建模型输入数据;然后构建Attention-GRU和Conv-Transformer模块相结合的并行计算结构,对数据周期性与连续性特征进行挖掘,融合特征后输出预测值;最后对2家酒店历史入住率数据进行参数调优以及消融与对比实验。实验表明:所提算法在预测精度上提升较大,同时能够充分满足时效性的时间要求,可用于酒店入住率数据实时预测,帮助酒店管理者及时调整经营战略,提升酒店竞争力。
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.
[1] SALAMANIS A, XANTHOPOULOU G, KEHAGIAS D, et al. LSTM-based deep learning models for long-term tourism demand forecasting[J]. Electronics,2022,11(22):3681.
[2] HU T, SONG H Y. Analysis of influencing factors and distribution simulation of budget hotel room pricing based on big data and machine learning from a spatial perspective[J]. Sustainability,2023,15(1):617.
[3] 张宏宏,甘旭升,吴亚荣,等.基于Monte-Carlo的无人机系统安全目标水平评估[J].电光与控制,2021,28(8):26-30.
ZHANG H H, GAN X S, WU Y R, et al. Evaluation of safety target level of UAV system based on monte-carlo simulation[J].Electronics Optics & Control,2021,28(8):26-30.
[4] HU B, YAN B. Analysis system of MICE tourism economic development strategy based on machine learning algorithm[J]. Mobile Information Systems,2022,2022: 1283040.
[5] WANG L X, SHUANG Y, ZHANG H J. Risk factors and prediction methods for endometriosis combined with ureteral stricture based on logistic-regression analysis[J]. Archivos Espanoles de Urologia,2023,76(3): 232-237.
[6] QI Y L, TIAN G, BAI M Z, et al. Study on construction deformation prediction and disaster warning of karst slopes based on grey theory[J]. Bulletin of Engineering Geology and the Environment,2023,82(2):62.
[7] 张斌儒,刘树林,张超锋,等.互联网环境下基于消费者搜索的酒店入住率预测研究[J].统计与信息论坛,2018,33(3):93-99.
ZHANG B R,LIU S L,ZHANG C F, et al. Forecasting hotel occupancy rate based on consumer search within network environment[J]. Statistics &Information Forum,2018,33(3):93-99.
[8] FENG Z Y, HU X L, TIAN Z G, et al. Bi-LSTM-based dynamic prediction model for pulling speed of czochralski single-crystal furnace[J]. Journal of Computing and Information Science in Engineering,2023,23(4): 041010.
[9] XU D H, ZHANG Q, DING Y, et al. Application of a hybrid ARIMA-LSTM model based on the SPEI for drought forecasting[J]. Environmental Science and Pollution Research,2022,29(3): 4128-4144.
[10] ZHANG J M, HOU X W, LIU S M, et al. New data-driven method for in situ coalbed methane content evolution: A BP neural network prediction model optimized by grey relation theory and particle swarm[J]. Energy & Fuels,2023,37(14):10344-10354.
[11] REN K Y, YAN T, HU Z X, et al. Image attention transformer network for indoor 3D object detection[J].Science China(Technological Sciences),2024,67(7):2176-2190.
[12] 徐凯旋,李宪,潘亚磊.基于双向编码转换器和文本卷积神经网络的微博评论情感分类[J].复杂系统与复杂性科学,2021,18(2):89-94.
XU K X,LI X,PAN Y L, et al. Weibo comments sentiment classification based on BERT and text CNN[J]. Complex Systems and Complexity Science,2021,18(2):89-94.
[13] 崔孝凯,王庆芝,刘其朋.基于数据驱动的锂电池健康状态预测[J].复杂系统与复杂性科学,2024,21(3):154-159.
CUI X K,WANG Q Z,LIU Q P. A data-driven model for prediction of lithium battery state of health[J]. Complex Systems and Complexity Science,2024,21(3):154-159.
[14] CHO K, MERRIENBOER B V, GULCEHRE C, et al. Learning phrase representations using RNN encoder decoder for statistical machine translation[J]. Computer Science, 2014:1-15.
[15] 张文娟,杨皓哲,张彬,等.考虑多时间尺度特征的城市轨道交通短时客流量预测模型[J].交通运输系统工程与信息,2022,22(6):212-223.
ZHANG W J,YANG H Z, ZHANG B, et al. Short-time passenger flow prediction model of urban rail transit considering multi-timescale features[J]. Journal of Transportation Systems Engineering and Information Technology,2022,22(6):212-223.
