A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19

TL;DR

This paper introduces DemandNet, a deep learning framework designed to accurately predict hotel demand and revenue during COVID-19, aiding managerial decisions amidst pandemic-related disruptions.

Contribution

The paper presents DemandNet, a novel interpretable deep learning model that incorporates feature selection and nonlinear modeling for pandemic-affected time series forecasting.

Findings

DemandNet outperforms existing models in accuracy.

It effectively captures COVID-19's impact on hotel demand.

The model provides interpretable insights into data trends.

Abstract

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.