{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-novel-deep-learning-model-for-hotel-demand","title":"A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19","arxiv_id":"2203.04383","date":"2022-03-08","proceeding":null,"authors":["Ashkan Farhangi","Arthur Huang","Zhishan Guo"],"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.","url_abs":"https://arxiv.org/abs/2203.04383v1","url_pdf":"https://arxiv.org/pdf/2203.04383v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-novel-deep-learning-model-for-hotel-demand","repo_url":"https://github.com/ashfarhangi/covid-19","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"covid-19-modelling","task_name":"COVID-19 Modelling"},{"task_slug":"covid-19-tracking","task_name":"COVID-19 Tracking"},{"task_slug":"correlated-time-series-forecasting","task_name":"Correlated Time Series Forecasting"},{"task_slug":"interpretability-techniques-for-deep-learning","task_name":"Interpretability Techniques for Deep Learning"},{"task_slug":"multivariate-time-series-forecasting","task_name":"Multivariate Time Series Forecasting"},{"task_slug":"new-product-sales-forecasting","task_name":"New Product Sales Forecasting"},{"task_slug":"probabilistic-deep-learning","task_name":"Probabilistic Deep Learning"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-prediction","task_name":"Time Series Prediction"},{"task_slug":"univariate-time-series-forecasting","task_name":"Univariate Time Series Forecasting"}],"methods":[{"method_slug":null,"method_name":null},{"method_slug":null,"method_name":"Travel"}],"datasets_introduced":[{"slug":"hotel-sales","name":"Hotel","full_name":"Hospitality > Tourism > Hotel Demand/Sales"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}