Abstract
Hotel order cancellation prediction has always been an influential part of hotel management. A better prediction model can optimize the accuracy of the prediction and thus enhance the value of subsequent business analysis and operational optimization. In this paper, a multidimensional hybrid evaluation prediction model Md-Pred is proposed for the first time. It combines the CatBoost, LGBM classifier, and SARIMAX time series algorithm, which can more effectively balance the influence of various features on classification problems as well as differentiate between objective features and subjective features. Results indicate that the performance of the prototype is significant, a new level of accuracy in predicting hotel order cancellations and future guest flow has been achieved.
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