Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/79549

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

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Title:A Novel Deep Learning Model For Hotel Demand and Revenue Prediction amid COVID-19
Authors:Farhangi, Ashkan
Huang, Arthur
Guo, Zhishan
Keywords:Machine Learning and Predictive Analytics in Accounting, Finance, and Management
anomalies in sequential data
covid-19 impact on tourism
nonlinear modeling
time series prediction
Date Issued:04 Jan 2022
Abstract:The COVID-19 pandemic has cast a substantial impact on 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. It is essential to develop interpretable forecasting models to support 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 DemandNet framework has the following unique characteristics. First, it selects the top static and dynamic features embedded in the time series data. Second, it includes a nonlinear model which can provide interpretable insight into the previously seen data. Third, a novel prediction model is developed to leverage the above characteristics to make robust long-term forecasts. We evaluated DemandNet using daily hotel demand and revenue data from eight cities in the US between 2013 and 2020. Our findings reveal that DemandNet outperforms the state-of-art models and can accurately predict the effect of the COVID-19 pandemic on hotel demand and revenue.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/79549
ISBN:978-0-9981331-5-7
DOI:10.24251/HICSS.2022.217
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Machine Learning and Predictive Analytics in Accounting, Finance, and Management


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