Demand forecasting model using hotel clustering findings for hospitality industry

作者：

Highlights：

• The growth of the tourism industry brings the importance of hotel demand forecasting.

• Attention-LSTM is used to perform for weekly hotel demand forecasting.

• NN Embeddings and K-Means findings are used to improve prediction model performance.

摘要

•The growth of the tourism industry brings the importance of hotel demand forecasting.•Attention-LSTM is used to perform for weekly hotel demand forecasting.•NN Embeddings and K-Means findings are used to improve prediction model performance.

论文关键词：Demand forecasting,Attention LSTM,Deep learning,Hotel clustering,Feature embedding

论文评审过程：Received 19 May 2021, Revised 23 September 2021, Accepted 4 November 2021, Available online 17 November 2021, Version of Record 20 November 2021.

论文官网地址：https://doi.org/10.1016/j.ipm.2021.102816