A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19
This provides an interpretable forecasting tool for hotel managers to support decision-making amid pandemic disruptions, though it is incremental as it applies a novel method to a specific domain.
The authors tackled the problem of predicting hotel demand and revenue during the COVID-19 pandemic by developing DemandNet, a deep learning framework that outperformed state-of-the-art models on data from eight U.S. cities.
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.