| dc.description.abstract | Tourism is an important and fast-growing industry in the global economy. In recent years, the tourism industry has faced many challenges that directly affect the hotel industry. Especially in the Internet age, online room booking platforms have changed consumer behavior and increased the frequency of cancellations. To deal with these problems, the hotel industry needs accurate demand forecasting tools. The purpose of this study is to establish a forecasting model for daily cancellation orders in hotels, and to explore the application of three different methods in this field, including classification models, time series models, and model hybriding.This study uses the real data sets of four hotels provided by the case company as research. Machine learning algorithms, such as Random Forest, XGBoost, and LGBM, are used in classification forecasting and applied to customer orders; in time series forecasting, simple moving average (SMA), periodic moving average (Periodic SMA) and LSTM Seq2Seq are used ; In terms of model hybriding, the weighted average is used to hybrid the prediction results of classification and time series.The results show that: In the classification model experiments of the four hotels, in addition to customer order features, adding date and time-related features and new features created by feature engineering can improve the prediction accuracy; In the time series model experiments, single For variable prediction, the deep learning model (LSTM Seq2Seq) is not more accurate than the traditional time series (Periodic SMA). The WMAPE of the four hotels using Periodic SMA dropped by about 46% on average. In addition, using LSTM Seq2Seq, its multivariate prediction results are better than univariate, and the average WMAPE of the four hotels drops by about 36%. A decrease of about 3.3%;The joint training of the data of the four hotels and considering the characteristics of the hotels can further improve the prediction accuracy, and the AUC of the four hotels has increased by about 1.6% on average. The analysis of this study shows that model hybriding helps to provide a comprehensive and effective hotel cancellation order forecasting solution for the hotel industry. | en_US |