论文标题
在COVID-19中，一个新颖的酒店需求和收入预测的深度学习模型
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19
论文作者
论文摘要
COVID-19大流行严重影响了旅游业和酒店业。诸如旅行限制和家庭订单之类的公共政策严重影响了旅游活动和服务业务的运营和盈利能力。为此，必须开发一个支持管理和组织决策的可解释的预测模型。我们开发了需求网，这是一个新型的深度学习框架，用于在Covid-19大流行的影响下预测时间序列数据。框架首先选择时间序列数据中嵌入的顶部静态和动态功能。然后，它包括一个非线性模型，该模型可以提供对先前看到的数据的可解释见解。最后，开发了一个预测模型，以利用上述特征来做出可靠的长期预测。我们使用来自美国八个城市的每日酒店需求和收入数据评估了框架。我们的发现表明，需求网络的表现胜过最先进的模型，并且可以准确预测Covid-19-19的大流行对酒店需求和收入的影响。
The COVID-19 pandemic has significantly impacted the tourism and hospitality sector. Public policies such as travel restrictions and stay-at-home orders had significantly affected tourist activities and service businesses' operations and profitability. To this end, it is essential to develop an interpretable forecast model that supports managerial and organizational decision-making. We developed DemandNet, a novel deep learning framework for predicting time series data under the influence of the COVID-19 pandemic. The framework starts by selecting the top static and dynamic features embedded in the time series data. Then, it includes a nonlinear model which can provide interpretable insight into the previously seen data. Lastly, a prediction model is developed to leverage the above characteristics to make robust long-term forecasts. We evaluated the framework using daily hotel demand and revenue data from eight cities in the US. Our findings reveal that DemandNet outperforms the state-of-art models and can accurately predict the impact of the COVID-19 pandemic on hotel demand and revenues.
