skittli / covid-19

A repository for COVID-19 factors and impacts on US economy

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

Overview

The COVID-19 pandemic has significantly impacted the tourism and hospitality sector, with public policies such as travel restrictions and stay-at-home orders affecting tourist activities and business operations. To address this, we developed DemandNet, a novel deep learning framework for predicting time series data under the influence of the COVID-19 pandemic. DemandNet aims to support managerial and organizational decision-making by providing accurate and interpretable forecasts.

Key Features

• Feature Selection: A mechanism to select the top static and dynamic features embedded in the time series data.
• Nonlinear Modeling: A multilayer neural network that provides interpretable insights into previously seen data.
• Robust Predictions: A prediction model leveraging selected features and nonlinear models to make robust long-term forecasts.
• Dynamic Dropout Optimization: Minimizes prediction uncertainties and provides optimal confidence in forecasts.

Contributions

1. Feature Selection Mechanism: Selects the top static and dynamic features of a time series, enhancing the ability to capture complex critical features.
2. Multilayer Neural Network: Derives the nonlinear relationship of selected features to the predictor, providing interpretable insights.
3. Novel Prediction Model: Leverages a dynamic dropout optimization mechanism for robust multi-step time series prediction.
4. Capability for New Data: Capable of predicting newly added time series data without previous training.

A repository for COVID-19 factors and impacts on US economy. To get a local copy up and running follow these simple example steps.

Datasets

Gathered State-level data:

loc: data/COVID19_state.xlsx

Prerequisites

• Tensorflow 2.0.2

Installation

1. Clone the repo

   git clone https://github.com/ashfarhangi/COVID-19.git

2. Install requirement packages

   pip install -r requirements.txt

3. Run model.py

Citation

@inproceedings{farhangidemand,
  title={A Novel Deep Learning Model For Hotel Demand and Revenue Prediction amid COVID-19},
  author={Farhangi, Ashkan and Huang, Arthur and Guo, Zhishan},
  booktitle={Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS 2022)},
  year={2022},
  organization={HICSS-55}
}