A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19 (2022-03-08T00:00:00.000000Z)

TL;DR

DemandNet, a novel deep learning framework for predicting time series data under the influence of the COVID-19 pandemic, outperforms the state-of-art models and can accurately predict the effect of theCOVID- 19 pandemic on hotel demand and revenue.

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. © 2022 IEEE Computer Society. All rights reserved.

Authors

Zhishan Guo

3 papers

Ashkan Farhangi

3 papers

Arthur Huang

3 papers

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

TL;DR

Abstract

Authors

References28 items

MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attention

Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data

A literature review of the economics of COVID‐19

Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review

A MACHINE LEARNING APPROACH BASED ON SVM FOR CLASSIFICATION OF LIVER DISEASES

The effect of large-scale anti-contagion policies on the COVID-19 pandemic

COVID-19 Machine Learning Based Survival Analysis and Discharge Time Likelihood Prediction Using Clinical Data

Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.0)

Forecasting the novel coronavirus COVID-19

How Does Household Spending Respond to an Epidemic? Consumption during the 2020 COVID-19 Pandemic

An interactive web-based dashboard to track COVID-19 in real time

Forecasting in social settings: The state of the art

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

Unsupervised Pre-training of a Deep LSTM-based Stacked Autoencoder for Multivariate Time Series Forecasting Problems

The State-of-the-Art in Twitter Sentiment Analysis

User's guide to correlation coefficients

Deep Mixed Effect Model Using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare

Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation

Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

A review of unsupervised feature learning and deep learning for time-series modeling

Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models

Long Short-Term Memory

Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models

“Data-driven solutions empowering the hospitality industry,”

Time-series Extreme Event Forecasting with Neural Networks at Uber

Statsmodels: Econometric and Statistical Modeling with Python

Forecasting time series with complex seasonal patterns using exponential smoothing

This Paper Is Included in the Proceedings of the 12th Usenix Symposium on Operating Systems Design and Implementation (osdi '16). Tensorflow: a System for Large-scale Machine Learning Tensorflow: a System for Large-scale Machine Learning

Field of Study

Venue Information

Name

Type

URL

Alternate Names